{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PsWDAqI0EFMo"
   },
   "source": [
    "https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
    "\n",
    "\n",
    "> The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u8NoV6kLKcVm"
   },
   "source": [
    "Libraries:\n",
    "----------\n",
    "- https://karateclub.readthedocs.io/en/latest/modules/root.html\n",
    "- networkx - https://networkx.github.io/documentation\n",
    "\n",
    "Reading materials:\n",
    "------------------\n",
    "- https://github.com/benedekrozemberczki/awesome-graph-classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8kVif7_1B4gP"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wm-jTFInDM_u"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lBqnzRuUDIbZ"
   },
   "outputs": [],
   "source": [
    "X, y = fetch_openml(data_id=1597, return_X_y=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please note that you will need a lot of RAM to run this notebook. If you want to reduce the size of RAM needed, you can work with smaller datasets as follows:\n",
    "\n",
    "```python\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, _, y, _ = train_test_split(X, y, test_size=0.33)\n",
    "```\n",
    "\n",
    "The above would get rid of 33% of the data. You can reduce it by more if you increase the test_size value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "I6rPOiKkCvgT",
    "outputId": "6e3d09ca-1817-462b-b7aa-9b5d6e5f6a80"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284807"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4Bv_eH8ACwFK",
    "outputId": "7913af0c-ce2d-49e3-a0b6-9383afbf9039"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 29)"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4NfTTUk2DjDV",
    "outputId": "91a91e84-0b7b-432c-c642-6b459acca6eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "92Xe5uwqDreR"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "o3wbJua7D1Ey",
    "outputId": "788e20e4-9b8f-4d42-ab6a-c77c58b2f3c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe72bf7e128>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X1X8leRS4P8k1wPPAR1r7ncClwDTwG+BjAFV1MMlNwKOt3Rer6mA7/hRwN3AK8L32kCQt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\nSJK6/h+CAp56d2V+ngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(y).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 158
    },
    "colab_type": "code",
    "id": "bwWR4L4QF6BK",
    "outputId": "bc5aae7d-b79d-470a-81af-21b596dcdd44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.16837498e-15,  3.41690805e-16, -1.37953671e-15,  2.07409512e-15,\n",
       "        9.60406632e-16,  1.48731301e-15, -5.55646730e-16,  1.20549793e-16,\n",
       "       -2.40630560e-15,  2.23885316e-15,  1.67332693e-15, -1.24701177e-15,\n",
       "        8.19000127e-16,  1.20729421e-15,  4.88745586e-15,  1.43751637e-15,\n",
       "       -3.74023697e-16,  9.56414917e-16,  1.03991661e-15,  6.40720156e-16,\n",
       "        1.65656173e-16, -3.56859322e-16,  2.61058162e-16,  4.47306595e-15,\n",
       "        5.21317980e-16,  1.68353699e-15, -3.65996607e-16, -1.22371013e-16,\n",
       "        8.83496193e+01])"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 158
    },
    "colab_type": "code",
    "id": "oXSRjImSGAFF",
    "outputId": "ce52c363-9c98-4e49-f9b5-39f039724794"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.95869237,   1.65130568,   1.51625234,   1.41586609,\n",
       "         1.38024431,   1.33226875,   1.23709143,   1.19435081,\n",
       "         1.09863016,   1.08884785,   1.02071124,   0.99919964,\n",
       "         0.99527248,   0.95859393,   0.9153144 ,   0.87625135,\n",
       "         0.84933557,   0.83817474,   0.81403907,   0.77092367,\n",
       "         0.73452272,   0.72570029,   0.6244592 ,   0.605646  ,\n",
       "         0.52127716,   0.48222617,   0.40363179,   0.33008268,\n",
       "       250.11967014])"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "O4au-CvGGXOD",
    "outputId": "f0eb76c7-f4ce-4999-db5a-c1dd6a315409"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting dython\n",
      "  Downloading https://files.pythonhosted.org/packages/99/3c/1002a46c098a2cd573da069c11d5d13dc1ceedd487d6aa8c92da7ff483be/dython-0.3.1-py3-none-any.whl\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from dython) (3.1.2)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from dython) (0.25.3)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from dython) (1.4.1)\n",
      "Requirement already satisfied: seaborn in /usr/local/lib/python3.6/dist-packages (from dython) (0.9.0)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from dython) (0.22.1)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from dython) (1.17.5)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->dython) (2.6.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->dython) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->dython) (2.4.6)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->dython) (1.1.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->dython) (2018.9)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->dython) (0.14.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->dython) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->dython) (42.0.2)\n",
      "Installing collected packages: dython\n",
      "Successfully installed dython-0.3.1\n"
     ]
    }
   ],
   "source": [
    "pip install dython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wrtg2jIXGSms"
   },
   "outputs": [],
   "source": [
    "from dython.nominal import associations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 881
    },
    "colab_type": "code",
    "id": "2S85bpGgGrzn",
    "outputId": "abd6ee1e-142a-46b7-c3db-1250d3870ca6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature correlations\n"
     ]
    },
    {
     "data": {
      "image/png": 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xuRKoN03T2cp7fU3TXHehE5w5cbGT0xMXK5w9cRERERH5d3D2xOVy+9tXS33y\nF+6zndj0jk//fhx083/7ZB7Pe4+LaZpHzvPeBSctIiIiIiIil4PXvsdFRERERETkUl3oqWIiIiIi\nInI5Nfrulzz6Mq24iIiIiIiIz9PERUREREREfJ4mLiIiIiIi4vN0j4uIiIiIiDeZusflUmjFRURE\nREREfJ4mLiIiIiIi4vO0VewcrPx2+0e2TrEstpXtFhEREfkmflQf/m03wTfocciXRCsuIiIiIiLi\n8zRxERERERERn6etYiIiIiIi3qStYpdEKy4iIiIiIuLzNHERERERERGfp61iIiIiIiJeZJoN33YT\nbEkrLiIiIiIi4vM0cREREREREZ+nrWIiIiIiIt6kp4pdEq24iIiIiIiIz/PqisuAyaO4rn9P6mpO\n8sH4eRTsOtSiTEK3a8l68X4CggI5sGo7Kye/DkBQRAjDXh5LxJVxlB8p4t0xszhZUe21+JczNoYf\nmI385tkZfLpuE9FRkfz9jbkt4pmmyXMz57Lms80EBbVn2sRx3HB9ZwDeXbacPy5YBMD92SO5IyvT\n6zm3Y+yk9B4MeHoUhr8feYtWs3HOUo+Y/oEBDJnxAAndk6gpdbFk7GwqjhQD0GfMUHqMyMBsaGTF\n5IUc+nSnR10rY9s133btS7u33Y79qdht5/qxa2xvxLfjWLncsbeM/gN15e7YiRk9uGnqKAw/P/a/\ntZo9sz3zHdfnu9w05S4ik69m/S9nc/j9Tc199d+30vXhHwDw+e//zsF31rRok7Q9Xltxua7/jUQl\nOchJH8dHT84n85l7Wi03aNq9fDjhT+SkjyMqyUFSRg/A/YHw5brd5GSM58t1u0kbM9Rr8S93bL8O\nkQD8ICuTuTOeOWfO1ny2ma+OHGPZ2/OZ/PhDTH1hNgDlFS7mvPYmb+XM5K2cmcx57U3KK1wt6tsp\nJ96IbfgZDJyazTvZ05k/8HGSh6UR06WjR8zuIzI4UV5FTvo4tsz/kIwJIwGI6dKR5KFpvJr5BO9k\nTyfzmXsw/IymelbGtmu+rYxtdb7t3HY79qdit53rx66xvRHfjmPFitg3jB3WlO/UZ+9h9U+nsyzj\nca654xbCu1zhEbP6aDEbf/1Hvvzbeo/jgZEhdHv0h3z8/Ul8NOS3dHv0h7SLCG61XT7LbPTtHx91\nwYmLYRg3G4bR+9SfbzAM41HDMLK+7ok6Z6by+eK1AORv209QeAgh8ZEeZULiIwkM7UD+tv0AfL54\nLV0G9QKgS2Yquxa7Z9O7Fq9pOu6N+Jc7tl+g++Lq1bM7EeFh58zZqrUbGHb7AAzD4MZuybhclRQV\nl7Bu41Zu6Z1CRHgYEeFh3GHeRJ8AACAASURBVNI7hXUbt3o153aMndizE2WHCig/XERjXQN7lm6g\nc2aqR9wumTc11f1i2Sau7tu1qU17lm6gobae8sNFlB0qILFnp6Z6Vsa2a76tjG11vu3cdjv2p2K3\nnevHrrG9Ed+OY8WK2Ffe7s5pdEonKg8VUPWVO99fvbuBK7/nme+qI8WU7TmM2Wh6HE/M6IHz053U\nllVRV16N89OddOx/I9L2nXfiYhjG08AfgDmGYTwHzAZCgAmGYUz8OicKc0RRcex402uXs4SwhCjP\nMglRuJwlzWXySwhzuMsEx4ZTVVgGQFVhGcGx4V6Lf7lj4+ffSoZaKig6jiM+tul1QnwsBUXFFBQV\n44iPaz4e5z5+NjvlxBuxQx1RuPJbr3NaqCOKimPuMmZDIydd1XSICiXs7LrOEkLPqGtlbCtzYtfY\nVufbzm23Y38qdtu5fuwa2xvx7ThWrIgdFBvhPu6IpvqMuNX5JXRIbPlZ3JoOjiiqjzWfrzq/hA6t\nfI5L23Ohe1z+C+gJtAecwJWmaVYYhvECsBGY1lolwzBGA6MBfhh9M31Cu1y+FouIiIiIPZkXLiJy\nLheauNSb7q/2rDYMY79pmhUApmnWGIZxzg1wpmnOA+YBvyr4/MtUAGfeAcI7xnD0VJkwRzSuglKP\neq6CUsIc0U2vwxKjcTndZaqLKwiJj6SqsIyQ+EiqiytIuXsgPUb2x4r4DbX1ZC+bZklsGi/u21IT\n4mJwFjavpBQUFpMQF0tCXCybt+U1Hy8qpneKey+pXXNiZezq4goAKp2lhCW2Xue0Smcp4R2jqXSW\nYPj70T4smJrSSlxn13VEU3lGXStiqy+935dW5tzKtmusKPbFxLbrZ6E3YlsVX9dmy9gnjpe7jztL\nCO4Y01QnODGamnzPuOdS4ywl/pZkj7qFn+25qLo+Q49DviQXusel1jCM03c7NW08NAwjAriYjL+8\nIGsiC7ImsvfjrXQd3g+AxJROnHRVNy0dnlZVWEZtZQ2JKe59oV2H92Pfcve9G/tW5NJt+K0AdBt+\nK3uXb2XbwhVYFX/X4jWWxW6s9Xwa2rlk9EtjyYcrMU2THbv2EBoaQlxsNH37pLJ+Uy7lFS7KK1ys\n35RL3z7u7rFrTqyMvffU8fwdB4hKchBxVRx+7fxJHprGvuW5HnHPrHt91s18tX63+/jyXJKHpuEf\nGEDEVXFEJTnI376/qZ4VsdWX3u9LK3NuZds1VhT72x6Dp9k1tlXxdW22jH30I3dOS7YfICzJQcip\nfF99RxpHPm55v25r8lfn4UjvTruIYNpFBONI707+6rwLVxTbM0zz3Gt2hmG0N03zZCvHY4FE0zRb\nf4boGaZfc1fTCQZOzSYpvQf1NbV8MH4ezp0HAcheNo0FWe5bZhzdkxj84mgCggI5uHoHKyYtBCAo\nMpQ7XnmQ8I4xlB8tZsmYWZwor/I4l5XxL2fsK5JDwWzksaefZ/O2PMrKKoiJjmTMfaOor68HYMSd\nQzBNk2kzXmHthi10CApi6lOP0C35OwD89b2PyFn4NgCjs0dy55BBALyUOsmWOfFW7Ov638htk+7C\n8Pdj558/YcPsJfR7dDjOvIPsW5GLf/t2DHnpARK6XsuJskqWjJ1N+eEiANLGDqP7j9Ix6xtZOeV1\nDp71IWllbLvm2659afe227E/FbvtXD92je2N+HYcK5c79tbRf6C2zB078bYbuel37sdPH1j0Cbv/\n8C7dHxtOyY6DHP04l+gbr+PW+Y8QGBlMw4k6ThSVs6z/E+6+GpnODQ+6n1D2+R/e5eDbnwLw42P/\n1/IxkT6oZuU8n94012HAaJ/M43knLpfDmRMXcXtk6xTLYp89cRERERHxFVfVWftroW0mLivm+vTv\nxx0GPuCTefTa97iIiIiIiIhcKk1cRERERETE513oqWIiIiIiInI56alil0QrLiIiIiIi4vM0cRER\nEREREZ+nrWIiIiIiIt5kaqvYpdCKi4iIiIiI+DxNXERERERExOdpq9i3wMovidSXW4qIiIivmlT3\nT0vj/9jS6JeRnip2SbTiIiIiIiIiPk8TFxERERER8XmauIiIiIiIiM/TPS4iIiIiIt6ke1wuiVZc\nRERERETE52niIiIiIiIiPk9bxUREREREvMnUVrFLoRUXERERERHxeZq4iIiIiIiIz9NWMRERERER\nb9JTxS6J1yYuSek9GPD0KAx/P/IWrWbjnKUe7/sHBjBkxgMkdE+iptTFkrGzqThSDECfMUPpMSID\ns6GRFZMXcujTnV6LfdqAyaO4rn9P6mpO8sH4eRTsOtSiTEK3a8l68X4CggI5sGo7Kye/DkBQRAjD\nXh5LxJVxlB8p4t0xszhZUW1JbAw/MBv5zbMz+HTdJqKjIvn7G3NbxDNNk+dmzmXNZ5sJCmrPtInj\nuOH6zgC8u2w5f1ywCID7s0dyR1Zmi/pW59yqfNt1HCq2d8eJnfOi2N4fK3aMrb5sPd/KufU5f/ve\nB6kodzXV++2zj5E+sC811Sd44qHJ7M77Z4vY89+eRVx8LAEB/mzZsI3JT/wPjY2NfLdrF6b8v6cI\nDgnm6OFjjHvgNy3qStvila1ihp/BwKnZvJM9nfkDHyd5WBoxXTp6lOk+IoMT5VXkpI9jy/wPyZgw\nEoCYLh1JHprGq5lP8E72dDKfuQfDz/BK7NOu638jUUkOctLH8dGT88l85p5W/56Dpt3LhxP+RE76\nOKKSHCRl9ADcHwpfrttNTsZ4vly3m7QxQy2L7dchEoAfZGUyd8Yz5+oS1ny2ma+OHGPZ2/OZ/PhD\nTH1hNgDlFS7mvPYmb+XM5K2cmcx57U3KK1weda3OuVX5tus4VGzvjhM750WxvT9W7Bhbfdl6vpVz\n7+T8/oea66cP7Ms1113FwJt/wG/HPcOU6U+2Gvvh+yYwrP+Pybr1R0THRjF42EAApr30W154Zhbf\nTx/B8mWr+PnYu1utL23H1564GIax8OvWSezZibJDBZQfLqKxroE9SzfQOTPVo0yXzJvYtXgNAF8s\n28TVfbsC0DkzlT1LN9BQW0/54SLKDhWQ2LOTV2Kf1jkzlc8XrwUgf9t+gsJDCImP9CgTEh9JYGgH\n8rftB+DzxWvpMqjXqfOnNp1/1+I1TcetiO0XGAxAr57diQgPa9kZp6xau4Fhtw/AMAxu7JaMy1VJ\nUXEJ6zZu5ZbeKUSEhxERHsYtvVNYt3GrR12rc25Vvu06DhXbu+PEznlRbO+PFTvGVl+2nm/l3Ds5\nH5iV0VRv4O3p/P3t9wHYvnUXYRGhxCXEcrbKyioAAgICaNeuHSYmAEmdrmHT+lwA1q7eyPe+f1uL\nuj7LbPTtHx913omLYRhLzvpZCvzw9OuLPUmoIwpXfknTa1d+CWGOqBZlKo65y5gNjZx0VdMhKpSw\ns+s6Swg9o66VsU8Lc0RRcey4R7mwBM9yYQlRuJyttyM4NpyqwjIAqgrLCI4Ntyw2fv4t2t+agqLj\nOOKbPxwS4mMpKCqmoKgYR3xc8/E49/EzWZ1zq/Jt13Go2N4dJ3bOi2J7f6zYMbb6svV8WxlfOW/O\nSWxcTFO5hMR48o8VNL12HiskwdH8O8iZXv3zbDbsWU5VZTUfLlkJwN5/7mfg4AwABg8biOOKhFbr\nSttxoRWXK4EKYAbw4qkf1xl/bpVhGKMNw9hiGMaWjZV7L1dbRURERMTGTNO8pHo/+9FY/qPb9whs\n345bbu0NwJMPT+Gn9/43f1vxBiGhwdTV1l3OpooPutDN+b2Ah4GJwGOmaW43DKPGNM1PzlfJNM15\nwDyA6dfcZVY6SwlLjG56PywxGpez1KNOpbOU8I7RVDpLMPz9aB8WTE1pJa6z6zqiqTyjrlWxU+4e\nSI+R/QFw5h0gvGMMR88o5yrwPIeroJQwR+vtqC6uICQ+kqrCMkLiI2morSd72TRLYtPYwMVIiIvB\nWdi8klJQWExCXCwJcbFs3pbXfLyomN4pPVrk83Ln3Mp8VxdXWNZuK3Oi2N/OOLFjXhTbu2PFys9w\nK2Pb9bPQ6ute//54N+d1dXUsWfUmAHnbdpPYsXmVxNExngJnEedSe7KWFR98woDB6az7ZCMH9h3i\n3h/9CoBrr7uajMx+REVHnrO+T9FTxS7JeVdcTNNsNE3zJeBeYKJhGLO5hCeR5e84QFSSg4ir4vBr\n50/y0DT2Lc/1KLNvRS7dht8KwPVZN/PV+t3u48tzSR6ahn9gABFXxRGV5CB/+37LY29buIIFWRNZ\nkDWRvR9vpevwfgAkpnTipKu6adnztKrCMmora0hMce8N7Tq8H/uWb21x/m7Db2XX4jWWxW6s9Xw6\nyrlk9EtjyYcrMU2THbv2EBoaQlxsNH37pLJ+Uy7lFS7KK1ys35RL3z6ee2WtyLmV+d576rgdx6Fi\ne3+c2DEviu3dsWLlZ7iVse36WWj1da9/f7yb878teo9h/X/CsP4/YcUHq/nBiCEA9EzthquikqIC\nz+3pwSEdmu578ff3JyOzHwf2HgIgOta9Hc0wDMY8eh+LFixG2jbj6yzZGYYxBOhrmuZTF1tn+jV3\nmeB+KsVtk+7C8Pdj558/YcPsJfR7dDjOvIPsW5GLf/t2DHnpARK6XsuJskqWjJ1N+WH3rDtt7DC6\n/ygds76RlVNe5+DqPI9zWBn7tIFTs0lK70F9TS0fjJ+Hc+dBALKXTWNB1kQAHN2TGPziaAKCAjm4\negcrJrmfYxAUGcodrzxIeMcYyo8Ws2TMLE6UV1kS+4rkUDAbeezp59m8LY+ysgpioiMZc98o6uvr\nARhx5xBM02TajFdYu2ELHYKCmPrUI3RL/g4Af33vI3IWvg3A6OyR3DlkEAAvpU7yWs6tyrddx6Fi\ne3ec2Dkviu39sWLH2OrL1vOtnFuf8/+690HKy5pXuJ/+nyf4z/7/QU3NCSY8NJldO/YAsGTVmwzr\n/xNi4qKZ938zCQwMxM/PYMO6LTz7mxk0NDSQPfrH/PRn/w3Ax++v4oWps9hbtLXl42F9UM3fnr+0\nPXNe0uHOCT6Zx681cbkUpycu4h2PbJ1iWewzJy4iIiIiX1dO9R5L49tm4vLXZ3369+MOP3zKJ/Po\nle9xERERERER+SY0cREREREREZ+niYuIiIiIiPi8r/2EMBERERER+Qb0OORLohUXERERERHxeZq4\niIiIiIiIz9NWMRERERERb9JWsUuiFRcREREREfF5WnFpY6z8kkh9uaWIiIh8EwsCrv22myA2pomL\niIiIiIg3mea33QJb0lYxERERERHxeZq4iIiIiIiIz9NWMRERERERb9JTxS6JVlxERERERMTnaeIi\nIiIiIiI+T1vFRERERES8SVvFLolWXERERERExOdp4iIiIiIiIj5PExcREREREfF5XrvHJSm9BwOe\nHoXh70feotVsnLPU433/wACGzHiAhO5J1JS6WDJ2NhVHigHoM2YoPUZkYDY0smLyQg59utNrsb0R\nf8DkUVzXvyd1NSf5YPw8CnYdalEmodu1ZL14PwFBgRxYtZ2Vk18HICgihGEvjyXiyjjKjxTx7phZ\nnKyotrTdv3l2Bp+u20R0VCR/f2Nui7aapslzM+ey5rPNBAW1Z9rEcdxwfWcA3l22nD8uWATA/dkj\nuSMrs0V9K3Ni19h2vX7seu3Yve0ah20jtpV9aWVsO+fEzm23Y39G9u9J0pSfgb8fhW+u5Ojsv3m8\nn3j/UBJ+MgCzvpG64+Xsf/QVTh4pIrjrtXR6fjT+YcGYDY0c+f1fOL5kfYu/r08zdY/LpfDKiovh\nZzBwajbvZE9n/sDHSR6WRkyXjh5luo/I4ER5FTnp49gy/0MyJowEIKZLR5KHpvFq5hO8kz2dzGfu\nwfAzvBLbG/Gv638jUUkOctLH8dGT88l85p5Wczho2r18OOFP5KSPIyrJQVJGD8D9ofDlut3kZIzn\ny3W7SRsz1PJ2/yArk7kznmm1nQBrPtvMV0eOsezt+Ux+/CGmvjAbgPIKF3Nee5O3cmbyVs5M5rz2\nJuUVrhb1rcqJXWPb9fqx67Vj97ZrHLaN2Fb2pZWx7ZwTO7fdlv3p58d1z/6C3T+dxvb0XxP7g350\n+M6VHrGrdh4k7/bH2THgUY6/t4FrfjMKgMaak+x9aBbbM37N7p9MJWnKz/APD2717yxty9eauBiG\n0c8wjEcNwxj0deol9uxE2aECyg8X0VjXwJ6lG+icmepRpkvmTexavAaAL5Zt4uq+XQHonJnKnqUb\naKitp/xwEWWHCkjs2ckrsb0Rv3NmKp8vXgtA/rb9BIWHEBIf6VEmJD6SwNAO5G/bD8Dni9fSZVCv\nU+dObTr3rsVrmo5b2e5ePbsTER7Guaxau4Fhtw/AMAxu7JaMy1VJUXEJ6zZu5ZbeKUSEhxERHsYt\nvVNYt3Fri/pW5cSuse16/dj12rF72zUO20ZsK/vSyth2zomd227H/gxN6UzNIScnvyrArKun+N21\nRH+vt0fsivW7aKypBaAy918EJsYAcOJAPicO5gNQV1BKXXE57WIikLbvvBMXwzA2nfHnXwCzgTDg\nacMwJlzsSUIdUbjyS5peu/JLCHNEtShTccxdxmxo5KSrmg5RoYSdXddZQugZda2M7Y34YY4oKo4d\n9ygTlnBWmYQoXM7W2xAcG05VYRkAVYVlBMeGe6Xd51NQdBxHfGzT64T4WAqKiikoKsYRH9d8PM59\n/GxW5cSuse16/dj12rF72zUO20Zs0DjRdd+2+7O9I5rao82/A9TmlxDoiOFc4n88gLJVuS2Oh/bs\njBEYwIlDznPW9UmNjb7946MutOLS7ow/jwYyTdP8HTAI+Om5KhmGMdowjC2GYWzZWLn3MjRTRERE\nRP4dxQ7/T0Jv7MTRV971ON4uPpIusx5i369ng2l+S60Tb7rQzfl+hmFE4Z7gGKZpFgGYplllGEb9\nuSqZpjkPmAcw/Zq7zEpnKWGJ0U3vhyVG43KWetSpdJYS3jGaSmcJhr8f7cOCqSmtxHV2XUc0lWfU\ntTK2VfFT7h5Ij5H9AXDmHSC8YwxHzyjjKvCM7yooJczRehuqiysIiY+kqrCMkPhIqosrvJKX80mI\ni8FZ2Py/KAWFxSTExZIQF8vmbXnNx4uK6Z3i3l9rZU4aauvJXjbNdrG90Zd2i+2Na8eObdc4bDux\nNU503bf1/ow/9fqks4TAK5p3ZwQmRlPrPM7ZIm7twZUPD+fzO3+LWdv8q6d/aAeS35jIV8+/SWWu\n/pP838WFVlwigK3AFiDaMIxEAMMwQgHjfBXPlL/jAFFJDiKuisOvnT/JQ9PYt9xzuW/fily6Db8V\ngOuzbuar9bvdx5fnkjw0Df/AACKuiiMqyUH+9v1eiW1V/G0LV7AgayILsiay9+OtdB3eD4DElE6c\ndFU3LdeeVlVYRm1lDYkp7r2hXYf3Y9/yrS3O3W34rew9ddzqvJxPRr80lny4EtM02bFrD6GhIcTF\nRtO3TyrrN+VSXuGivMLF+k259O3j3itrZU52LV5jy9je6Eu7xfbGtWPHtmsctp3YGie67tt6f55W\nuX0fHZISaX9VPEa7AGLv6EfJR1s8Yod0S6LT9Pv5Z/bz1B1vnmQa7QK4/tXHKXpnNcff34AtmaZv\n//gow7yExhmGEQwkmKZ58EJlp19zlwnuJ17cNukuDH8/dv75EzbMXkK/R4fjzDvIvhW5+Ldvx5CX\nHiCh67WcKKtkydjZlB8uAiBt7DC6/ygds76RlVNe5+DqPI9zWBnbG/EHTs0mKb0H9TW1fDB+Hs6d\n7rRmL5vGgqyJADi6JzH4xdEEBAVycPUOVkxaCEBQZCh3vPIg4R1jKD9azJIxszhRXmVJux/ZOgWA\nx55+ns3b8igrqyAmOpIx942ivt79vyAj7hyCaZpMm/EKazdsoUNQEFOfeoRuyd8B4K/vfUTOwrcB\nGJ09kjuHuJ/z8FLqJK/kxK6x7Xr92PXasXvbNQ7bRmyNE133bbE/+9XWNLU58rabSJpyL4a/HwWL\n/sHR3y/mqsdGUrljH6Ufb+GGt58mOPlq6k6tHp08Wsw/73me2OH/SeeXfkXNF4ebYu399WyqPz/E\nf+Qvvuj/WP821SyY4LuzA6BD9vM+mcdLmrh8HacnLmJ/pycuVjh74iIiIiJtz5kTFyto4nJ5+OrE\nxWtfQCkiIiIiIvj0k7t8mVe+gFJEREREROSb0MRFRERERER8nraKiYiIiIh4k7aKXRKtuIiIiIiI\niM/TxEVERERERHyeJi4iIiIiIuLzdI+LiIiIiIg3mbrH5VJoxUVERERERHyeVlzkoln57faPbJ1i\nWWwr2y0iIiIXr/MNxd92E8TGNHEREREREfEis9H8tptgS9oqJiIiIiIiPk8TFxERERER8XnaKiYi\nIiIi4k2NeqrYpdCKi4iIiIiI+DxNXERERERExOdpq5iIiIiIiDfpCygviVZcRERERETE52niIiIi\nIiIiPs9rW8WS0nsw4OlRGP5+5C1azcY5Sz3e9w8MYMiMB0jonkRNqYslY2dTccT97ap9xgylx4gM\nzIZGVkxeyKFPd7Z6jgGTR3Fd/57U1Zzkg/HzKNh1qEWZhG7XkvXi/QQEBXJg1XZWTn4dgKCIEIa9\nPJaIK+MoP1LEu2NmcbKi2ittt6rddoqN4QdmI795dgafrttEdFQkf39jbot4pmny3My5rPlsM0FB\n7Zk2cRw3XN8ZgHeXLeePCxYBcH/2SO7IymxR3045aQtj0K7ttnPbrWy3XWPbNd9Wtlux29Z1b2Vs\nK3MS2PtmQn/1IPj5cWLZ+1QverNFmwHa3/qfREyeSskvR1P/ry8ACP7xTwkanAWNjVTO/gO1Wza3\nWtdn6QsoL4lXVlwMP4OBU7N5J3s68wc+TvKwNGK6dPQo031EBifKq8hJH8eW+R+SMWEkADFdOpI8\nNI1XM5/gnezpZD5zD4af0eIc1/W/kagkBznp4/joyflkPnNPq20ZNO1ePpzwJ3LSxxGV5CApowfg\nvri+XLebnIzxfLluN2ljhnql7Va1226x/TpEAvCDrEzmznim1VgAaz7bzFdHjrHs7flMfvwhpr4w\nG4DyChdzXnuTt3Jm8lbOTOa89iblFa4W9e2UE7uPQbu2285tt7Lddo1t13xb2W7FblvXvZWxLc2J\nnx9hD/2asicfp+Rn2bS/bQD+11zTos1Ghw50+OF/Ubf786Zj/tdcQ/v+t1Fy3z2UTXiMsIcfAT9t\nIvp3cN5eNgyjj2EY4af+3MEwjN8ZhrHUMIz/MQwj4mJPktizE2WHCig/XERjXQN7lm6gc2aqR5ku\nmTexa/EaAL5Ytomr+3YFoHNmKnuWbqChtp7yw0WUHSogsWenFufonJnK54vXApC/bT9B4SGExEd6\nlAmJjyQwtAP52/YD8PnitXQZ1OvU+VObzr9r8Zqm41a33ap22y22X2AwAL16diciPIxzWbV2A8Nu\nH4BhGNzYLRmXq5Ki4hLWbdzKLb1TiAgPIyI8jFt6p7Bu49YW9e2UE7uPQbu2285tt7Lddo1t13xb\n2W7FblvXvZWxrcxJwHeTqT96lMb8fKiv5+Sqf9D+P/pxtpB776N60ZuYtbVNx9r/Rz9OrvoH1NXR\n6HRSf/QoAd9NblFX2p4LTU9fBU6vQ/4eiAD+59Sx1y72JKGOKFz5JU2vXfklhDmiWpSpOOYuYzY0\nctJVTYeoUMLOrussIfSsugBhjigqjh33KBeW4FkuLCEKl7P1dgTHhlNVWAZAVWEZwbHhXmm7Ve22\nW2z8/LkYBUXHccTHNr1OiI+loKiYgqJiHPFxzcfj3MfPZqec2H0M2rXddm67le22a2ywZ76tbLdi\nt63r3srYVubEPzaWxqLCpteNRUX4xTb/+w4Q0KULfnHx1G7c4HHcLzaWhjPrFhfhf1Zdn9fY6Ns/\nPupC97j4maZZf+rPvUzTvOnUn9cahrH9XJUMwxgNjAb4YfTNfIfe37ylIiIiIvLvwTAIfeBXVEx/\n/ttuifiQC01cdhmGca9pmq8BOwzD6GWa5hbDML4D1J2rkmma84B5ANOvucusdJYSlhjd9H5YYjQu\nZ6lHnUpnKeEdo6l0lmD4+9E+LJia0kpcZ9d1RFN5qm7K3QPpMbI/AM68A4R3jOHoGeVcBZ7ncBWU\nEuZovR3VxRWExEdSVVhGSHwk1cUVTe263G23st0NtfVkL5tmu9g0NnAxEuJicBY2r6QUFBaTEBdL\nQlwsm7flNR8vKqZ3invvrl3zbdcxaPd227ntVn3W2jG2XfNt188ru8a283Vv139/TmsoLsYvLr7p\ntV9cHI3Fzf++G8HBBCQlETVjpvv96Ggipj5L+W+forG4GP8z68bG0VDccpeFtD0X2ir2cyDdMIz9\nwA3AZ4ZhHAByTr13UfJ3HCAqyUHEVXH4tfMneWga+5bnepTZtyKXbsNvBeD6rJv5av1u9/HluSQP\nTcM/MICIq+KISnKQv929P3PbwhUsyJrIgqyJ7P14K12Hu/dGJqZ04qSrumnZ87SqwjJqK2tITHHv\nsew6vB/7lm9tcf5uw29l76njVrTdynbvWrzGlrEba6u5GBn90ljy4UpM02THrj2EhoYQFxtN3z6p\nrN+US3mFi/IKF+s35dK3j3sfrl3zbdcxaPd227ntVn3W2jG2XfNt188ru8a283Vv139/Tqv/5z8J\nuOJK/BwOCAigff/bOLl+XdP7ZlUVxT+8g+M/Hcnxn46kbvduyn/7FPX/+oKT69fRvv9t0K4dfg4H\nAVdcSf0/9yBtn2GaF34c26kb9JNwr9AcMU2z4GJPMP2au0xwP/Hitkl3Yfj7sfPPn7Bh9hL6PToc\nZ95B9q3Ixb99O4a89AAJXa/lRFklS8bOpvxwEQBpY4fR/UfpmPWNrJzyOgdX57V6roFTs0lK70F9\nTS0fjJ+Hc+dBALKXTWNB1kQAHN2TGPziaAKCAjm4egcrJi0EICgylDteeZDwjjGUHy1myZhZnCiv\nwhttt6rddop9RXIoLOrPQwAAIABJREFUmI089vTzbN6WR1lZBTHRkYy5bxT19e7diiPuHIJpmkyb\n8QprN2yhQ1AQU596hG7J3wHgr+99RM7CtwEYnT2SO4cMAuCl1Em2z7ddx6Bd223ntlvZbrvGtmu+\nrR7jit12rns7jfF7Oh9uanPgzX0I/dWDGH5+1HywjOo33yDknp9R98U/qf1svUfuIl+cSeUf5zQ/\nDvknd9FhcBZmQwOVr8ymdtNGAOJXftLy0bM+qPr3D/j085CDH57rk3m8qInLN3F64iL/n717j6+q\nuvP//1oJhEBycr8cMloNFx3kmkY0DtiEQqgNX7CWGaUdaez4HXQobceCV/pFyqU6/BTpiIKk1AGt\nl1o6FR1EgYpykfsdbIergpCQmMs5SbiFrN8fJ4TcIBTYx7PT9/PxyOPh2Xutz/7w2fucZLnW3kcu\n5qHNUxyL3XTgIiIiIl+NhgMXJ2jgcnWE6sBFD70WEREREZGQ19rN+SIiIiIicjU5vOKprdKMi4iI\niIiIhDwNXEREREREJORpqZiIiIiISDCF8LfThzLNuIiIiIiISMjTwEVEREREREKeloqJiIiIiART\nrZ4qdjk04yIiIiIiIiFPMy4SEpz8dvuHNk9xLLaTeYuIiLQ10/Z7HY3/n45Gl6+aBi4iIiIiIsFk\n9VSxy6GlYiIiIiIiEvI0cBERERERkZCngYuIiIiIiIQ83eMiIiIiIhJMehzyZdGMi4iIiIiIhDwN\nXEREREREJORpqZiIiIiISBDZWj0O+XJoxkVEREREREJeUGdcBk8eTZdB/Thz4hTvTZhH0a5Dzdqk\n9rqevGcfoF1kBAc+3MaKya8AEBkbxYgXxhF7TTIVR4p5e+zznPJVBy2+U7HTs/sw+MnRmPAwdryx\nkvVz3mkUMzyiHcNmPkhq73ROlPlZPG42viMlANw6djh97snBnq1l+eSFHPp4Z6O+TsZ2U70xYWBr\n+fkvZ/Lxmg0kxMfxx1fnNotnreWpWXNZ9clGIiM7MH3ieG66sRsAby9ZxksL3gDggfxR3JmX26y/\nW+utazD4nymqeXA/Z3Uu205sJ8+l0/FV80BN5vzoOU74qur7jXzyPm4alMHpE6f47YQ5HNl9sFns\nYRPu4ZbvfoNOsdE83DO/fnu7iHbcO/NHXNurC1Xlfv5r3K+a9ZW2JWgzLl0G9SU+3UtB9njef3w+\nudPua7Hd0Ok/ZOljv6Ygezzx6V7Sc/oAgTfWZ2v2UJAzgc/W7CFr7PCgxXcqtgkzDJmaz1v5M5g/\n5BF6jMgisXtao5i978nhZEUVBdnj2TR/KTmPjQIgsXsaPYZn8ZvcR3krfwa50+7DhJn6fk7Gdlu9\nwzrGAfCdvFzmzpzWYiyAVZ9s5PMjR1ny5nwmP/ITpj4zG4AKn585L7/G6wWzeL1gFnNefo0Kn79R\nX7fWW9dg8D9TVPPgfs46Gdut59KtsZ08l07HV83P1yR37J31fW7K6UdyupepOT/lzScKuHv6/S3G\n3r1iC8/eObHZ9qy7v0l1RRVTc37KyvlLGPHY91vsH5JqbWj/hKiLDlyMMT8xxlx7NQ7ULTeT3YtW\nA3Bs634iY6KISolr1CYqJY6I6I4c27ofgN2LVtN96M0AdM/NZNeiVQDsWrSqfnsw4jsVu3O/rpQf\nKqLicDG1Z87y6Tvr6Jab2Shu99yv1/f9y5INfG1Az/qcPn1nHWdP11BxuJjyQ0V07te1vp+Tsd1W\n77CITgDc3K83sTEeLuTD1esYccdgjDH07dUDv7+S4pJS1qzfzG39M4iN8RAb4+G2/hmsWb+5UV+3\n1lvXYPA/U1Tz4H7OOhnbrefSrbGdPJdOx1fNz9ekd27/+n69h/Znwx8+BuDQ1r109EQRk9w49rl9\nvuLyZtt7D72ZDYs+AmDbknXc8A+9AEyzhtJmtDbjMhVYb4xZZYwZa4xJvtwDebzx+I5+Wf/aX1iK\nJzW+cZvUePyFpefbHCvF4w206ZQUQ9XxwEVbdbycTkkxQYvvVOxobzz+Yy33OSfaG4/vaKCNPVvL\nKX81HeOj8TTtW1hKdIO+TsZ2siZOxCYsnEtRVPwl3pSk+tepKUkUFZdQVFyCN+X8pZ+aHNjekFvr\nrWsw+J8pqnlwP2edjO3Wc+nW2KD3fUt1cVvNPcmx9e1iU+MpbxC7vPBLYr0JXKrY1IT6/rVnaznp\nrwZIvOQAckWMMXcYY/5ijNlnjHnsAm3uNsbsMcbsNsa8dqXHbG3gcgC4hsAAJhPYY4xZaozJN8Zc\n8H9dG2PGGGM2GWM2ra/ce6U5ioiIiEhbYEN3GVJQ2drQ/mmFMSYceAH4NnAT8D1jzE1N2nQHHgcG\nWGt7Av9+pWVr7eZ8a62tBT4APjDGtK9L8HvAM0CLMzDW2nnAPOBHRbs/ywQo3HGAmLREvqhr4/Em\n4C8qa9TPX1SGp8FI29M5AX9hoE11iY+olDiqjpcTlRJHdYmPjB8Moc+oQTgR/+zpGvKXTHckdnWJ\nD4DKwjI8nVvuc05lYRkxaQlUFpZiwsPo4OnEibJK/E37ehOobNDXidhurTe1Z7kUqcmJFB4/P5NS\ndLyE1OQkUpOT2Lh1x/ntxSX0z+jTrJZuqreuweB/pqjmwX3f6zO87cTW+75t1bzmdA2PLPkPAD7f\nvp+4tPMTJHHeRCoazNy0pqKolLi0RMoLSwkLDyPS0wngy9b6yVVxC7DPWnsAwBjzBnAnsKdBm38F\nXrDWlgFYa49f6UFbm3FptE7QWnvGWrvYWvs94LpLiP/CgryJLMibyN4PNtNz5EAAOmd05ZS/un7q\n8Jyq4+WcrjxB54zA+sqeIweyb1ngXoJ9y7fQa+TtAPQaeTt7l21m68LlOBV/16JVjsXeW7f92PYD\nxKd7ib02mbD24fQYnsW+ZVsaxW3Y98a8W/h8beB62LdsCz2GZxEe0Y7Ya5OJT/dybNv++n5OxHZr\nvWtPN35izIXkDMxi8dIVWGvZvutToqOjSE5KYMCtmazdsIUKn58Kn5+1G7Yw4NbG64fdVm9dg8H/\nTFHNg/u+12d424mt933bqvmGRR8zI+9RZuQ9yo4PNnLLd78BwPUZ3Tnpr27xXpYL2bVsE7eMzAag\nX14We9fuBtCUzlXQcPVU3c+YJk3+Djjc4PWRum0N3QDcYIxZY4xZZ4y544rzsheZsjPG3GCt/d8r\nOcCM6+6tP8CQqfmkZ/eh5sRp3pswj8KdgUfe5S+ZzoK8wNMivL3T+fazY2gXGcHBldtZPmkhAJFx\n0dz54o+JSUuk4osSFo99npMVVY2O5WR8p2J3GdSXb066FxMexs7ffcS62YsZ+LORFO44yL7lWwjv\n0J5hzz1Ias/rOVleyeJxs6k4XAxA1rgR9L47G1tTy4opr3Bw5flZAadju6nef9cjGmwtDz/5NBu3\n7qC83EdiQhxj7x9NTU0NAPfcNQxrLdNnvsjqdZvoGBnJ1CceolePGwD4w7vvU7DwTQDG5I/irmFD\nAXguc5Lr661rMPifKap5cD9ndS7bTmynrxNdK87XfO6PZlLdoOb/NOVf6JHdl9MnTvPbh+dweOcB\nAB5Z8h/MyHsUgBGP/TM33zmAmNR4fEVlfPLmn3hv1u9p16E9o2eO45qe11NdXsl//fhXPLnqeVfc\nnF815Z9DeoAVNem3F62jMeYfgTustf+37vVo4FZr7bgGbd4FzgB3E7j15GOgt7X20kenTY97sYHL\n1dBw4CLyVXho8xTHYjccuIiIiMjFHTFnHI3/n4fe1MDlKriEgcttwGRr7bfqXj8OYK19qkGbucB6\na+3Lda9XAI9Zazdebl5B+x4XERERERFpEzYC3Y0x6caYCGAUsLhJmz8COQDGmCQCS8cOXMlBW7s5\nX0RERERErqba1p/cFcqstTXGmHHA+0A48Btr7W5jzBRgk7V2cd2+ocaYPcBZ4GFr7RU9PEEDFxER\nERER+atYa5cAS5psm9Tgvy3ws7qfq0JLxUREREREJORp4CIiIiIiIiFPS8VERERERIKpNqQfKhay\nNOMiIiIiIiIhTwMXEREREREJeVoqJiIiIiISTNbdj0P+qmjgIm2ek99u/9DmKY7FBmdzFxERCbY4\n/ekpV0BLxUREREREJORp2CsiIiIiEkx6qthl0YyLiIiIiIiEPA1cREREREQk5GmpmIiIiIhIENla\nPVXscmjGRUREREREQp4GLiIiIiIiEvK0VExEREREJJj0VLHLohkXEREREREJeUGbcUnP7sPgJ0dj\nwsPY8cZK1s95p9H+8Ih2DJv5IKm90zlR5mfxuNn4jpQAcOvY4fS5Jwd7tpblkxdy6OOdLR5j8OTR\ndBnUjzMnTvHehHkU7TrUrE1qr+vJe/YB2kVGcODDbayY/AoAkbFRjHhhHLHXJFNxpJi3xz7PKV+1\n47m7NbaT9Xbrufz5L2fy8ZoNJMTH8cdX5zbL11rLU7PmsuqTjURGdmD6xPHcdGM3AN5esoyXFrwB\nwAP5o7gzLzdo9XbrdeJ03m6ti1tr7tbYbq23k3m7ObZqHvzrMO/JH9B9UF/OnDjNf094iWO7m+fd\nudf1fPeZB2kX2Z69H25nyS8WNtr/D/83jzt+/s88nfFAs77StgRlxsWEGYZMzeet/BnMH/IIPUZk\nkdg9rVGb3vfkcLKiioLs8Wyav5Scx0YBkNg9jR7Ds/hN7qO8lT+D3Gn3YcJMs2N0GdSX+HQvBdnj\nef/x+eROu6/FXIZO/yFLH/s1BdnjiU/3kp7TBwi8uT5bs4eCnAl8tmYPWWOHO567W2M7WW+3nkuA\n7+TlMnfmtBZzBVj1yUY+P3KUJW/OZ/IjP2HqM7MBqPD5mfPya7xeMIvXC2Yx5+XXqPD5g1Jvt14n\nTuft1rq4teZuje3WejuZt5tjq+bBvw675/QlMd3Lr3LGs/iJ+Qyf/sMW8x4+7V94+/Ff86uc8SSm\ne+me07d+X0znBLp9ozfldYMladsuOnAxxkQYY35gjBlS9/r7xpjZxpgfGWPaX+pBOvfrSvmhIioO\nF1N75iyfvrOObrmZjdp0z/06uxatAuAvSzbwtQE9AeiWm8mn76zj7OkaKg4XU36oiM79ujY7Rrfc\nTHYvWg3Asa37iYyJIiolrlGbqJQ4IqI7cmzrfgB2L1pN96E31x0/s/74uxatqt/uZO5uje1kvZ2M\n7XRNbu7Xm9gYDxfy4ep1jLhjMMYY+vbqgd9fSXFJKWvWb+a2/hnExniIjfFwW/8M1qzfHJR6u/U6\ncTpvt9bFrTV3a2y31tvJvN0cWzUP/nX490Mz2faHQN8jW/cR6elEdHLjvKOT4+jg6ciRrfsA2PaH\nVfz90PM5fPv/jeb9p17H4rJ7RmptaP+EqNZmXF4GhgE/Nca8AvwTsB7oD/z6Ug8S7Y3Hf6y0/rX/\nWCkeb3yzNr6jgTb2bC2n/NV0jI/G07RvYSnRTfoCeLzx+I5+2aidJ7VxO09qPP7ClvPolBRD1fFy\nAKqOl9MpKcbx3N0aG5yrt5Oxg3EdXkxR8Zd4U5LqX6emJFFUXEJRcQnelOTz25MD2xv9e11aE7fm\n7da6OBnbrZ9Xbj2Xbs3bzbFV8+BfhzGpCVQ0yNtXWEpM0zbeeHwN4viOlRKTmgDA3+dm4isqpejT\nz5G/Da3d49LbWtvHGNMO+AJIs9aeNca8Cmy/UCdjzBhgDMB3E27hBvpftYRFRERE5G9b+8gIvvGj\nESwY/fRXnYoEUWsDlzBjTAQQBXQCYoFSoANwwaVi1tp5wDyAGdfdaysLy/B0Tqjf7+mcgL+wrFGf\nysIyYtISqCwsxYSH0cHTiRNllfib9vUmUFnXN+MHQ+gzahAAhTsOEJOWyBcN2vmLGh/DX1SGx9ty\nHtUlPqJS4qg6Xk5UShzVJb76vJzI3Y2xnaz32dM15C+Z7kjsYJzLS5GanEjh8fMzKUXHS0hNTiI1\nOYmNW3ec315cQv+MPoRFxriyJm5/X7qxLm5//7gttluvcbdeg8G4vlXz4F2Ht4zOJfN7gZp8sf0A\nsWmJ9W1ivAn4msT3FZYR0yBOTOcEfEWlxF+XStw1yYx976n6vg++Ox3ACxQS6mztV52BK7W2VGw+\n8GdgGzAReMsYUwBsBN641IMc236A+HQvsdcmE9Y+nB7Ds9i3bEujNvuWb6HXyNsBuDHvFj5fuyew\nfdkWegzPIjyiHbHXJhOf7uXYtsD6zK0Ll7MgbyIL8iay94PN9Bw5EIDOGV055a+un/Y8p+p4Oacr\nT9A5I7DGsufIgexbtrnZ8XuNvJ29ddudyt2NsZ2s965Fq1x9Li9FzsAsFi9dgbWW7bs+JTo6iuSk\nBAbcmsnaDVuo8Pmp8PlZu2ELA27NpPakz5U1cfv70o11cfv7x22x3XqNu/UaDMb1rZoH7zrc8Moy\n5uQ9wZy8J/jzB5vo991A32syunHSf4LK4sZ5VxaXc8p/gmsyAk/h7Pfd2/nzB5s5/pfDzLh5LM8N\n/HeeG/jv+ApLmft/JoIbBi1y2Yy1F78BxxiTBmCtPWqMiQOGAJ9bazdcygFmXHevhcATL7456V5M\neBg7f/cR62YvZuDPRlK44yD7lm8hvEN7hj33IKk9r+dkeSWLx82m4nAxAFnjRtD77mxsTS0rprzC\nwZU7WjzWkKn5pGf3oebEad6bMI/CnQcByF8ynQV5EwHw9k7n28+OoV1kBAdXbmf5pMAj9SLjornz\nxR8Tk5ZIxRclLB77PCcrqnA6d7fGdrLebjqXD22eUp/zw08+zcatOygv95GYEMfY+0dTU1MDwD13\nDcNay/SZL7J63SY6RkYy9YmH6NXjBgD+8O77FCx8E4Ax+aO4a9hQAJ7LnOS6mgTrOnE6b7fWxa01\nd2tst9bbrdeg07FVc+evw0rT+O/OYVPuo3t2n8DjkB9+iaN1ef/bkl8yJ+8JANJ6p3PXMw/QPjKC\nvSu38z9PLmhW24dWz+Kl4T/nsa0vNX/0bAiqnHBn6N4BD0Q/83ZI1rHVgcuVOjdwEWmLGg5cnNBw\n4CIiIuJ2TQcuV9uUQ78NyT+4m6r82YiQ/vs4eubikKxjUL7HRURERERE5Epo4CIiIiIiIiGvtaeK\niYiIiIjIVWRD+EseQ5lmXEREREREJORp4CIiIiIiIiFPS8VERERERIJJS8Uui2ZcREREREQk5Gng\nIiIiIiIiIU8DFxERERERCXm6x0XkCjj9zfYPbZ7iWGyncxcREWmqnJqvOoXQUFv7VWfgSppxERER\nERGRkKeBi4iIiIiIhDwtFRMRERERCSY9DvmyaMZFRERERERCngYuIiIiIiIS8rRUTEREREQkmLRU\n7LJoxkVEREREREKeBi4iIiIiIhLytFRMRERERCSIrNVSscsRtIFLenYfBj85GhMexo43VrJ+zjuN\n9odHtGPYzAdJ7Z3OiTI/i8fNxnekBIBbxw6nzz052LO1LJ+8kEMf72zxGIMnj6bLoH6cOXGK9ybM\no2jXoWZtUntdT96zD9AuMoIDH25jxeRXAIiMjWLEC+OIvSaZiiPFvD32eU75qoOSuxvzdmtsJ+vt\nROxzfv7LmXy8ZgMJ8XH88dW5zWJaa3lq1lxWfbKRyMgOTJ84nptu7AbA20uW8dKCNwB4IH8Ud+bl\nBq0mbj6Xbr3GFbvtfKYo7+C/752O78bYTp/PkU/ex02DMjh94hS/nTCHI7sPNmszbMI93PLdb9Ap\nNpqHe+bXb28X0Y57Z/6Ia3t1oarcz3+N+1WzvtK2BGWpmAkzDJmaz1v5M5g/5BF6jMgisXtaoza9\n78nhZEUVBdnj2TR/KTmPjQIgsXsaPYZn8ZvcR3krfwa50+7DhJlmx+gyqC/x6V4Kssfz/uPzyZ12\nX4u5DJ3+Q5Y+9msKsscTn+4lPacPEHhzfbZmDwU5E/hszR6yxg4PSu5uzNutsZ2st9Oxv5OXy9yZ\n01qMB7Dqk418fuQoS96cz+RHfsLUZ2YDUOHzM+fl13i9YBavF8xizsuvUeHzB6Umbj6Xbr3GFbvt\nfKYo7+C/752O78bYTp/Pm3L6kZzuZWrOT3nziQLunn5/i3nvXrGFZ++c2Gx71t3fpLqiiqk5P2Xl\n/CWMeOz7LfaXtiMoA5fO/bpSfqiIisPF1J45y6fvrKNbbmajNt1zv86uRasA+MuSDXxtQE8AuuVm\n8uk76zh7uoaKw8WUHyqic7+uzY7RLTeT3YtWA3Bs634iY6KISolr1CYqJY6I6I4c27ofgN2LVtN9\n6M11x8+sP/6uRavqtzuduxvzdmtsJ+vtdOyb+/UmNsbDhXy4eh0j7hiMMYa+vXrg91dSXFLKmvWb\nua1/BrExHmJjPNzWP4M16zcHpSZuPpduvcYVu+18pijv4L/vnY7vxthOn8/eQ/uz4Q8fA3Bo6146\neqKISW6c97l9vuLyZtt7D72ZDYs+AmDbknXc8A+9AJr/3+1QVGtD+ydEtTpwMcZ0McZMMMb8yhgz\n0xjzoDEm5q85SLQ3Hv+x0vrX/mOleLzxzdr4jgba2LO1nPJX0zE+Gk/TvoWlRDfpC+DxxuM7+mWj\ndp7Uxu08qfH4C1vOo1NSDFXHA2+KquPldEqKCUrubszbrbHBuXo7Hbs1RcVf4k1Jqn+dmpJEUXEJ\nRcUleFOSz29PDmxvlJMLr0En83Y6d8VuO7FB75+2kncw4rsxttPnMzY1nvIGeZcXfkmsN4FLFZua\nUN+/9mwtJ/3VAImXHEBc56IDF2PMT4C5QCTQH+gAXAusM8bkOJ6diIiIiIgIrd+c/69AP2vtWWPM\nTGCJtTbHGPMS8DaQ0VInY8wYYAzAdxNuIa2wG57O50fQns4J+AvLGvWpLCwjJi2BysJSTHgYHTyd\nOFFWib+wrHFfbwKVdX0zfjCEPqMGAVC44wAxaYl80aCdv6jxMfxFZXi8LedRXeIjKiWOquPlRKXE\nUV3iq8/raufu1rwb9nNTbCfrffZ0DflLpjsSu7rEhyeWS5KanEjh8fMzKUXHS0hNTiI1OYmNW3ec\n315cQv+MPoRFxjiaN7jvXJ7L26ncFbvtxHbrZ7jybjlvt/6OcPr3z7lzdbXP5+2jh3Lb9wYD8Pn2\n/cSlnZ8gifMmUtFgVqg1FUWlxKUlUl5YSlh4GJGeTgBfttYvJITwcqxQdin3uJwb3HQAogGstZ8D\n7S/UwVo7z1p7s7X25luju3Ns+wHi073EXptMWPtwegzPYt+yLY367Fu+hV4jbwfgxrxb+HztnsD2\nZVvoMTyL8Ih2xF6bTHy6l2PbAuszty5czoK8iSzIm8jeDzbTc+RAADpndOWUv7p+2vOcquPlnK48\nQeeMwBrLniMHsm/Z5mbH7zXydvbWbXcid7fmfY7bYjtZ712LVjl+Li9FzsAsFi9dgbWW7bs+JTo6\niuSkBAbcmsnaDVuo8Pmp8PlZu2ELA27NpPakz5XXYDDeO07lrthtJ7ZbP8OVd8t5u/V3RDB+/zhx\nPle98gEz8h5lRt6j7PhgI7d89xsAXJ/RnZP+6hbvZbmQXcs2ccvIbAD65WWxd+1uAI0I2jBzsedI\nG2N+CtwPrAduB/7DWvuyMSYZWGSt/UZrB5hx3b0WAk+8+OakezHhYez83Uesm72YgT8bSeGOg+xb\nvoXwDu0Z9tyDpPa8npPllSweN5uKw8UAZI0bQe+7s7E1tayY8goHV+5o8VhDpuaTnt2HmhOneW/C\nPAp3Bh6pl79kOgvyAk+j8PZO59vPjqFdZAQHV25n+aSFAETGRXPniz8mJi2Rii9KWDz2eU5WVBGM\n3N2Yt1tjO1lvJ2L/258eBeDhJ59m49YdlJf7SEyIY+z9o6mpqQHgnruGYa1l+swXWb1uEx0jI5n6\nxEP06nEDAH94930KFr4JwJj8Udw1bCgAz2VOcvU16PS5dOs1rtht5zNFeQf/fe90fDfGvtrn84g5\n06je/zTlX+iR3ZfTJ07z24fncHjnAQAeWfIfzMgL/A4c8dg/c/OdA4hJjcdXVMYnb/6J92b9nnYd\n2jN65jiu6Xk91eWV/NePf8WTq553xc35vvtzQ3qAFTN/WUjW8aIDFwBjTE+gB7DLWvvnv/YA5wYu\nIvLXe2jzFMdiNxy4iIiIBEPTgcvV9p+H3gzJP7ib0sDl8rT6BZTW2t3A7iDkIiIiIiLS5lnd43JZ\ngvI9LiIiIiIiIldCAxcREREREQl5rS4VExERERGRq0hLxS6LZlxERERERCTkaeAiIiIiIiIhT0vF\nRERERESCqfarTsCdNOMiIiIiIiIhTwMXEREREREJeVoqJhLCnPx2+4c2T3EstpN5i4iIe11j23/V\nKYQEfQHl5dGMi4iIiIiIhDwNXEREREREJORpqZiIiIiISDBpqdhl0YyLiIiIiIiEPA1cREREREQk\n5GngIiIiIiIiIU/3uIiIiIiIBFPtV52AO2nGRUREREREQp4GLiIiIiIiEvKCtlQsPbsPg58cjQkP\nY8cbK1k/551G+8Mj2jFs5oOk9k7nRJmfxeNm4ztSAsCtY4fT554c7Nlalk9eyKGPdwYtdjDiD548\nmi6D+nHmxCnemzCPol2HmrVJ7XU9ec8+QLvICA58uI0Vk18BIDI2ihEvjCP2mmQqjhTz9tjnOeWr\ndn3NVRPna3LOz385k4/XbCAhPo4/vjq3WUxrLU/NmsuqTzYSGdmB6RPHc9ON3QB4e8kyXlrwBgAP\n5I/izrxcx/MOxrl0c+6KHdzPFCdju7Xebo19jhuvFbfGDsb5DFVWj0O+LEGZcTFhhiFT83krfwbz\nhzxCjxFZJHZPa9Sm9z05nKyooiB7PJvmLyXnsVEAJHZPo8fwLH6T+yhv5c8gd9p9mDATlNjBiN9l\nUF/i070UZI/n/cfnkzvtvhZrOHT6D1n62K8pyB5PfLqX9Jw+QOCN+9maPRTkTOCzNXvIGjvc9TVX\nTYJXE4Dv5OUyd+a0FuMBrPpkI58fOcqSN+cz+ZGfMPWZ2QBU+PzMefk1Xi+YxesFs5jz8mtU+PxB\nydvpers1d8WPuIFgAAAgAElEQVQO/vtH10nbiO30+VTsr+ZzXNqeoAxcOvfrSvmhIioOF1N75iyf\nvrOObrmZjdp0z/06uxatAuAvSzbwtQE9AeiWm8mn76zj7OkaKg4XU36oiM79ugYldjDid8vNZPei\n1QAc27qfyJgoolLiGrWJSokjIrojx7buB2D3otV0H3pz3bEz64+9a9Gq+u1urrlqEryaANzcrzex\nMR4u5MPV6xhxx2CMMfTt1QO/v5LiklLWrN/Mbf0ziI3xEBvj4bb+GaxZvzkoeTtdb7fmrtjBf//o\nOmkbsc9x47Xi1tjBOJ/S9gRl4BLtjcd/rLT+tf9YKR5vfLM2vqOBNvZsLaf81XSMj8bTtG9hKdEN\n+joZOxjxPd54fEe/bNTGk9qkTWo8/sKWc+iUFEPV8XIAqo6X0ykpxvG8VZO2U5NLUVT8Jd6UpPrX\nqSlJFBWXUFRcgjcl+fz25MD2YOTtdL3dmrtiB//9o+ukbcQ+x43XiltjB+N8hrTaEP8JUbo5X0RE\nREREQt5Fb843xsQCjwPfAVIACxwH3gaettaWX6DfGGAMwHcTbiGtsBuezgn1+z2dE/AXljXqU1lY\nRkxaApWFpZjwMDp4OnGirBJ/YVnjvt4EKhv0rWy6/yrGdip+xg+G0GfUIAAKdxwgJi2RLxq08Rc1\nju8vKsPjbTmH6hIfUSlxVB0vJyoljuoSn+N1UU3aRk08sVyS1ORECo+fn0kpOl5CanISqclJbNy6\n4/z24hL6Z/QhLDKG/CXTHcv7XC2dOJe6DttObCfP5dnTNa69xhW7eWy3XitujR2Ma1zartZmXH4H\nlAE51toEa20iMKhu2+8u1MlaO89ae7O19uZbo7tzbPsB4tO9xF6bTFj7cHoMz2Lfsi2N+uxbvoVe\nI28H4Ma8W/h87Z7A9mVb6DE8i/CIdsRem0x8updj2/bX93MytlPxty5czoK8iSzIm8jeDzbTc+RA\nADpndOWUv7p+SvWcquPlnK48QeeMwPrNniMHsm/Z5mbH7jXydvbWbXdbzVWTr6YmlyJnYBaLl67A\nWsv2XZ8SHR1FclICA27NZO2GLVT4/FT4/KzdsIUBt2ZSe9Ln2nOp67DtxHbyXO5atErXSRuK7dZr\nxa2xg3GNu4GttSH9E6qMtRdOzhjzF2vtjX/tvoZmXHevhcBTKb456V5MeBg7f/cR62YvZuDPRlK4\n4yD7lm8hvEN7hj33IKk9r+dkeSWLx82m4nAxAFnjRtD77mxsTS0rprzCwZU7Gh3DydjBiD9kaj7p\n2X2oOXGa9ybMo3DnQQDyl0xnQd5EALy90/n2s2NoFxnBwZXbWT5pIQCRcdHc+eKPiUlLpOKLEhaP\nfZ6TFVWO562auL8m//anRwF4+Mmn2bh1B+XlPhIT4hh7/2hqamoAuOeuYVhrmT7zRVav20THyEim\nPvEQvXrcAMAf3n2fgoVvAjAmfxR3DRsKwHOZk1x9Lt2cu2IH9zNF10nbie3ma8WtsZ04n4989qor\nHi9Weld26I4OgIT//igk69jawOUDYDmwwFpbVLctFbgPyLXWDmntAOcGLiISWh7aPMWx2A0HLiIi\nIsGigcvVEaoDl9aWit0DJAIfGWNKjTGlwEogAfgnh3MTEREREWl7vuqnhrn0qWIXvTnfWlsGPFr3\n04gx5ofAyw7lJSIiIiIiUu9KHof8i6uWhYiIiIiIyEW09jjklu9cAwOkXv10RERERETaNhvCy7FC\n2UUHLgQGJ98i8Pjjhgyw1pGMREREREREmmht4PIuEG2t3dZ0hzFmpSMZiYiIiIiINNHazfn3X2Tf\n969+OiIiIiIiIs21NuMiIiIiIiJXk+5xuSxX8lQxERERERGRoNCMi8jfKCe/3f6hzVMci+1k3iIi\n4ixNNMiV0MBFRERERCSI9Djky6OlYiIiIiIiEvI0cBERERERkZCnpWIiIiIiIsGkpWKXRTMuIiIi\nIiIS8jRwERERERGRkKelYiIiIiIiQaSnil0ezbiIiIiIiEjI08BFRERERERCnpaKiYiIiIgEkZaK\nXZ6gDVzSs/sw+MnRmPAwdryxkvVz3mm0PzyiHcNmPkhq73ROlPlZPG42viMlANw6djh97snBnq1l\n+eSFHPp4Z4vHGDx5NF0G9ePMiVO8N2EeRbsONWuT2ut68p59gHaRERz4cBsrJr8CQGRsFCNeGEfs\nNclUHCnm7bHPc8pXHZTc3Zi3Yredc+lE3uf8/Jcz+XjNBhLi4/jjq3ObxbTW8tSsuaz6ZCORkR2Y\nPnE8N93YDYC3lyzjpQVvAPBA/ijuzMsNWr3dWHO3XisNa+7Gmrg1turdck3cnLsbzyfAkMmj6VqX\n9/9cJO9hzz5A+8gI9n+4jeV1ed+YdwsDH/ouSd3SWDDiSQp3HmzWV9qWoCwVM2GGIVPzeSt/BvOH\nPEKPEVkkdk9r1Kb3PTmcrKiiIHs8m+YvJeexUQAkdk+jx/AsfpP7KG/lzyB32n2YMNPsGF0G9SU+\n3UtB9njef3w+udPuazGXodN/yNLHfk1B9nji072k5/QBAm+uz9bsoSBnAp+t2UPW2OFByd2NeSt2\n2zmXTuYN8J28XObOnNZiPIBVn2zk8yNHWfLmfCY/8hOmPjMbgAqfnzkvv8brBbN4vWAWc15+jQqf\nP2h5u7Xmbs3brTVxa2zVu+WauDl3N57Phnm/lD2epY/P51sXyPtbdXm/VJd3l7q8S/73CP/9wK84\nvP4vLfaTticoA5fO/bpSfqiIisPF1J45y6fvrKNbbmajNt1zv86uRasA+MuSDXxtQE8AuuVm8uk7\n6zh7uoaKw8WUHyqic7+uzY7RLTeT3YtWA3Bs634iY6KISolr1CYqJY6I6I4c27ofgN2LVtN96M11\nx8+sP/6uRavqtzuduxvzVuy2cy6dzBvg5n69iY3xcCEfrl7HiDsGY4yhb68e+P2VFJeUsmb9Zm7r\nn0FsjIfYGA+39c9gzfrNQcvbrTV3a95urYlbY6veLdfEzbm78XyeP24g76Nb99PhAnl3iO7I0bq8\ndzXI+8t9Ryk9cAz52xGUgUu0Nx7/sdL61/5jpXi88c3a+I4G2tiztZzyV9MxPhpP076FpUQ36Qvg\n8cbjO/plo3ae1MbtPKnx+AtbzqNTUgxVx8sBqDpeTqekmKDk7sa8FbvtnEsn874URcVf4k1Jqn+d\nmpJEUXEJRcUleFOSz29PDmwPVt5urblb83ZrTdwaG1Tvlmri5tzdeD7P5e2/grzdzNaG9k+ouuyB\nizHmvYvsG2OM2WSM2bS+cu/lHkJERERERARo5eZ8Y8zXL7QL6HehftbaecA8gBnX3WsrC8vwdE6o\n3+/pnIC/sKxRn8rCMmLSEqgsLMWEh9HB04kTZZX4m/b1JlBZ1zfjB0PoM2oQAIU7DhCTlsgXDdr5\nixofw19Uhsfbch7VJT6iUuKoOl5OVEoc1SW++ryudu5uzbthP8V297l0Om9PLJckNTmRwuPnZ1KK\njpeQmpxEanISG7fuOL+9uIT+GX0Ii4whf8l0x/JuWE+31dyNeZ89XeP4+XTbZ4qTsd16DQaj3m7M\n3a3n8+s/GELfuryP7TiAJy2xUZu/Jm/529PajMtG4Bng2SY/zwBxF+nXyLHtB4hP9xJ7bTJh7cPp\nMTyLfcu2NGqzb/kWeo28HQg8JeLztXsC25dtocfwLMIj2hF7bTLx6V6ObQusc9y6cDkL8iayIG8i\nez/YTM+RAwHonNGVU/7q+mnPc6qOl3O68gSdMwJrLHuOHMi+ZZubHb/XyNvZW7fdidzdmrfT59ON\nsd16LoOR96XIGZjF4qUrsNayfdenREdHkZyUwIBbM1m7YQsVPj8VPj9rN2xhwK2Z1J70BSVvt9bc\nbXnvWrTKlTU5x22x3XoNBqPebszdredzy8LlvJw3kZfr8u5Vl3faRfI+VXmCtLq8e40c+Ff9nglZ\n1oT2T4gy1toL7zRmF3CXtbbZei9jzGFr7bWtHWDGdfdaCDw54puT7sWEh7Hzdx+xbvZiBv5sJIU7\nDrJv+RbCO7Rn2HMPktrzek6WV7J43GwqDhcDkDVuBL3vzsbW1LJiyiscXLmjxWMNmZpPenYfak6c\n5r0J8+ofi5e/ZDoL8iYC4O2dzrefHUO7yAgOrtzO8kkLAYiMi+bOF39MTFoiFV+UsHjs85ysqCIY\nubsxb8VuO+fSibz/7U+PAvDwk0+zcesOyst9JCbEMfb+0dTU1ABwz13DsNYyfeaLrF63iY6RkUx9\n4iF69bgBgD+8+z4FC98EYEz+KO4aNhSA5zInOV5vN9bcrddKw5q7sSZuja16t1wTN+fulvPZ9PaJ\n3Kn5dMnuw5kTp1nSIO8fLpnOyw3yHlaX94GV21lWl/cN37qZIb/4AZ0SPJzyVVO05zO6ZPcJ3b+6\nGyjKybnwH+AhIHXlypCsY2sDl38Edlprmz1nzhjzHWvtH1s7wLmBi4j87Xho8xTHYjccuIiIiLs4\nfd/3Y5+9GpJ/cDelgcvlueg9Ltba319kt/sf6SAiIiIiEmSh/OSuUHYlj0P+xVXLQkRERERE5CJa\ne6pYyzeTBJ4qlnr10xEREREREWnuogMXAoOTbwFNnztngLWOZCQiIiIi0obZ2pC8hSTktTZweReI\nttZua7rDGLPSkYxERERERESaaO3m/Psvsu/7Vz8dERERERGR5lqbcRERERERkatITxW7PFfyVDER\nEREREZGg0IyLiFx1Tn5JpL7cUkTEvdqF9NcuSqjTjIuIiIiIiIQ8zbiIiIiIiASRtXoc8uXQjIuI\niIiIiIQ8DVxERERERCTkaamYiIiIiEgQ6XHIl0czLiIiIiIiEvI0cBERERERkZCnpWIiIiIiIkFk\na/VUscuhGRcREREREQl5GriIiIiIiEjIC+pSscGTR9NlUD/OnDjFexPmUbTrULM2qb2uJ+/ZB2gX\nGcGBD7exYvIrAETGRjHihXHEXpNMxZFi3h77PKd81UGL71Ts9Ow+DH5yNCY8jB1vrGT9nHcaxQyP\naMewmQ+S2judE2V+Fo+bje9ICQC3jh1On3tysGdrWT55IYc+3hnUmit22ziXbsv7nJ//ciYfr9lA\nQnwcf3x1brOY1lqemjWXVZ9sJDKyA9MnjuemG7sB8PaSZby04A0AHsgfxZ15uc36u60uwTifbo2t\neuv3Q1v/W8LJ2E5eh9dn9+GbkwOxd76xkg0vNo/97ecCsU+W+XnnR4HYkXHRjJj7E7x9u7D7rY9Z\nMWlhs39rqLP2q87AnYI249JlUF/i070UZI/n/cfnkzvtvhbbDZ3+Q5Y+9msKsscTn+4lPacPELj4\nP1uzh4KcCXy2Zg9ZY4cHLb5TsU2YYcjUfN7Kn8H8IY/QY0QWid3TGsXsfU8OJyuqKMgez6b5S8l5\nbBQAid3T6DE8i9/kPspb+TPInXYfJqzxekk31sStsd16Lt2aN8B38nKZO3Nai/EAVn2ykc+PHGXJ\nm/OZ/MhPmPrMbAAqfH7mvPwarxfM4vWCWcx5+TUqfP5Gfd1aFyfzdmts1Vu/H9r63xJOxnb8Gp+W\nz6L8Gbw8+BH+/iKx539jPJt+vZRvPB6IffbUGdY8+3s+mv5ai/9OabuCNnDplpvJ7kWrATi2dT+R\nMVFEpcQ1ahOVEkdEdEeObd0PwO5Fq+k+9GYAuudmsmvRKgB2LVpVvz0Y8Z2K3blfV8oPFVFxuJja\nM2f59J11dMvNbBS3e+7X6/v+ZckGvjagZ31On76zjrOna6g4XEz5oSI69+vq+pq4NbZbz6Vb8wa4\nuV9vYmM8XMiHq9cx4o7BGGPo26sHfn8lxSWlrFm/mdv6ZxAb4yE2xsNt/TNYs35zo75urYuTebs1\ntuqt3w9t/W8JJ2M7eR16+3Wl7FARFZ8HYv/5nXV0Hdo4dtehX2f37wOx/7dB7DMnTvHFxv+l5uQZ\n5G9L0AYuHm88vqNf1r/2F5biSY1v3CY1Hn9h6fk2x0rxeANtOiXFUHW8HICq4+V0SooJWnynYkd7\n4/Efa7nPOdHeeHxHA23s2VpO+avpGB+Np2nfwlKim/R1Y03cGtut59KteV+KouIv8aYk1b9OTUmi\nqLiEouISvCnJ57cnB7Y3/Te7sS5O5u3W2KB66/dD2/5bwsnYTl6HHm88/qPn91ceayHnBm3s2VpO\n18VuC2ytCemfUHXRgYsxJsYY85Qx5hVjzPeb7HvxIv3GGGM2GWM2ra/ce7VyFRERERGRv1Gt3Zz/\nMrAXWAT8izFmJPB9a+0pIOtCnay184B5wI+Kdn+WCVC44wAxaYl8UdfG403AX1TWqJ+/qAyPN6H+\ntadzAv7CQJvqEh9RKXFUHS8nKiWO6hIfGT8YQp9Rg3Ai/tnTNeQvme5I7OoSHwCVhWV4Orfc55zK\nwjJi0hKoLCzFhIfRwdOJE2WV+Jv29SZQWVjm2pq4NbZbz6Wb8/bEcklSkxMpPH5+JqXoeAmpyUmk\nJiexceuO89uLS+if0afZv9ltdXEqbydronq3nZq4+TNcdflqrnF/YRmetPP7ozu3kHNdm3OxI+pi\ny9+u1paKdbXWPmat/aO1dgSwBfiTMSbxEuO/sCBvIgvyJrL3g830HDkQgM4ZXTnlr66fljyn6ng5\npytP0DkjsAay58iB7FsWWHu+b/kWeo28HYBeI29n77LNbF24HKfi71q0yrHYe+u2H9t+gPh0L7HX\nJhPWPpwew7PYt2xLo7gN+96Ydwufr90T2L5sCz2GZxEe0Y7Ya5OJT/dybNt+19bErbHdei7dnvel\nyBmYxeKlK7DWsn3Xp0RHR5GclMCAWzNZu2ELFT4/FT4/azdsYcCtjddVu7UuTuTtZE1U77ZTEzd/\nhqsuX801Xtgk9t8Pz2J/k9j7l22h5z8GYt+QdwuH62K3BV/1UjC3LhUz9iLPYzPGfAr0tNbWNth2\nH/AwEG2tva61A8y47t76AwyZmk96dh9qTpzmvQnzKNx5EID8JdNZkDcRAG/vdL797BjaRUZwcOV2\nltc94i4yLpo7X/wxMWmJVHxRwuKxz3OyoqrRsZyM71TsLoP68s1J9wYeBfi7j1g3ezEDfzaSwh0H\n2bd8C+Ed2jPsuQdJ7Xk9J8srWTxuNhWHiwHIGjeC3ndnY2tqWTHlFQ6uPP9/kd1cE7fGduu5dFve\n//anRwF4+Mmn2bh1B+XlPhIT4hh7/2hqamoAuOeuYVhrmT7zRVav20THyEimPvEQvXrcAMAf3n2f\ngoVvAjAmfxR3DRsKwHOZk+rzdltdgnE+3Rpb9dbvh7b+t4SbrvGwBn92pg/qy6An7yUsPIydb37E\n+tmLGfCzkRTuPMj+ZYHYebMeJKUu9rvjZlPxeSD2v655jghPR8Lbt+OUr5rf3/s0X+49yoTPXw3d\nv7obONQvN6QfiHz9tmUhWcfWBi4zgA+stcubbL8DeN5a2721AzQcuIiIXKmHNk9xLHbDgYuIiFx9\nYQ7/VaiBy9URqgOXi97jYq195ALblxpjfulMSiIiIiIiIo1dyeOQf3HVshARERER+RthbWj/hKqL\nzrgYY5oviq3bBaRe/XRERERERESaa+1xyKnAt4CyJtsNsNaRjERERERERJpobeDyLoGnh21rusMY\ns9KRjERERERE2rBQfuRwKGvt5vz7L7Lv+1c/HRERERERkeau5OZ8ERERERGRoGhtqZiIiIiIiFxF\n1mqp2OXQwEVEXMXJL4nUl1uKiDgrMoQftSuhT0vFREREREQk5GnGRUREREQkiGztV52BO2nGRURE\nREREQp4GLiIiIiIiEvI0cBERERERCaJaa0L651IYY+4wxvzFGLPPGPNYC/sfNMbsNMZsM8asNsbc\ndKV108BFREREREQumTEmHHgB+DZwE/C9FgYmr1lre1tr+wEzgJlXelwNXERERERE5K9xC7DPWnvA\nWnsaeAO4s2EDa62vwcso4Iofhq2niomIiIiISD1jzBhgTINN86y18xq8/jvgcIPXR4BbW4jzI+Bn\nQATwzSvNSwMXEREREZEgspd4H8lXpW6QMq/Vhq3HeQF4wRjzfeDnQP6VxNNSMRERERER+Wt8AVzb\n4PU1ddsu5A3gO1d60KDNuKRn92Hwk6Mx4WHseGMl6+e802h/eEQ7hs18kNTe6Zwo87N43Gx8R0oA\nuHXscPrck4M9W8vyyQs59PHOFo8xePJougzqx5kTp3hvwjyKdh1q1ia11/XkPfsA7SIjOPDhNlZM\nfgWAyNgoRrwwjthrkqk4UszbY5/nlK86KLm7MW+3xla9VZNLif3zX87k4zUbSIiP44+vzm1WC2st\nT82ay6pPNhIZ2YHpE8dz043dAHh7yTJeWvAGAA/kj+LOvNxm/Z2suZOxdR0GN2+31sStsZ2uuZOx\n3Vrzr+X04fbJgdh7Xl/Jlhcbxw6LaEfurAdJ7p3OyTI/74+djf9ICSn9ujDo6fsBMAY2PPffHFi6\nqfnJFCdtBLobY9IJDFhGAd9v2MAY091au7fu5TBgL1coKDMuJswwZGo+b+XPYP6QR+gxIovE7mmN\n2vS+J4eTFVUUZI9n0/yl5Dw2CoDE7mn0GJ7Fb3If5a38GeROuw8T1nx6rcugvsSneynIHs/7j88n\nd9p9LeYydPoPWfrYrynIHk98upf0nD5A4M312Zo9FORM4LM1e8gaOzwoubsxb7fGVr1Vk0utyXfy\ncpk7c1qLdQBY9clGPj9ylCVvzmfyIz9h6jOzAajw+Znz8mu8XjCL1wtmMefl16jw+Zv1d6rmTsbW\ndRjcvN1aE7fG1vn8as5n9rR83vnBDF775iPccGcW8U1i3zQqh1PlVbx6+3i2/3op//BEIHbpn4/w\nu2H/jzfvmMji0f8fOU/9EBPurkVEttaE9E+r+VtbA4wD3gc+BX5nrd1tjJlijBlR12ycMWa3MWYb\ngftcrmiZGARp4NK5X1fKDxVRcbiY2jNn+fSddXTLzWzUpnvu19m1aBUAf1myga8N6AlAt9xMPn1n\nHWdP11BxuJjyQ0V07te12TG65Waye9FqAI5t3U9kTBRRKXGN2kSlxBER3ZFjW/cDsHvRaroPvbnu\n+Jn1x9+1aFX9dqdzd2Pebo2teqsml1qTm/v1JjbGw4V8uHodI+4YjDGGvr164PdXUlxSypr1m7mt\nfwaxMR5iYzzc1j+DNes3N+vvVM2djK3rMLh5u7Umbo3tdM2djO3Wmqf260rFoSJ8nwdi7128ji5D\nG8fuMvTr/Pn3gdj7/mcD19TFrjl5Gnu2FoDwDu2vwrOq5HJYa5dYa2+w1na11k6v2zbJWru47r9/\naq3taa3tZ60dZK3dfaXHDMrAJdobj/9Yaf1r/7FSPN74Zm18RwNt7NlaTvmr6Rgfjadp38JSopv0\nBfB44/Ed/bJRO09q43ae1Hj8hS3n0Skphqrj5QBUHS+nU1JMUHJ3Y95ujQ2qt2py6Z8pF1NU/CXe\nlKT616kpSRQVl1BUXII3Jfn89uTA9qacqrmTsXUdBjdvt9bErbHP0fkMXs2jvPH4j57fX3mslKgm\nsRu2sWdrOe2vJjI+GggMfL63/Gm+t+wpVj7xcv1ARtq2iw5cjDFeY8wcY8wLxphEY8zkum/A/J0x\npvNF+o0xxmwyxmxaX3nFy9lEREREROoVbdvP60Me463/M4nMHw0PzLy4iLWh/ROqWrs5/7+A/yHw\npTEfAr8F8gg8FWAuTb5o5pyGj1Cbcd29trKwDE/nhPr9ns4J+AvLGvWpLCwjJi2BysJSTHgYHTyd\nOFFWib9pX28ClXV9M34whD6jBgFQuOMAMWmJ9Y8z8HgT8Bc1Poa/qAyPt+U8qkt8RKXEUXW8nKiU\nOKpLfPV5Xe3c3Zp3w35uiq16qyZ/TexLkZqcSOHx8zMpRcdLSE1OIjU5iY1bd5zfXlxC/4zA+nQn\na372dA35S6Y7ElufhcHN263nsmE/t8XW+fxqzmdVYRmetPP7ozsnUNUk9rk2VXWxIzydOFlW2ahN\n2b6jnKk6SeKN13B8x0GkbWttqViqtfZ5a+3TQJy19j+stYettc8D113qQY5tP0B8upfYa5MJax9O\nj+FZ7Fu2pVGbfcu30Gvk7QDcmHcLn6/dE9i+bAs9hmcRHtGO2GuTiU/3cmxbYO3n1oXLWZA3kQV5\nE9n7wWZ6jhwIQOeMrpzyV9dPqZ5Tdbyc05Un6JwRWGPZc+RA9i3b3Oz4vUbezt667U7k7ta8nT6f\nqrdqEgrX4KXIGZjF4qUrsNayfdenREdHkZyUwIBbM1m7YQsVPj8VPj9rN2xhwK2BNdtO1nzXolWu\nPJ9uvQ51LtvO7wfQe/OrOp9F2w8Qe70XT13s7iOyONgk9sFlW/j7fwzE7jbsFo6sCcT2XJtcfzO+\n5+8Sie+Whu9wMdL2GXuR+SBjzHZrbd+6/55mrf15g307rbW9WzvAjOvutRB4msY3J92LCQ9j5+8+\nYt3sxQz82UgKdxxk3/IthHdoz7DnHiS15/WcLK9k8bjZVNRdhFnjRtD77mxsTS0rprzCwZU7WjzW\nkKn5pGf3oebEad6bMI/CnYGRd/6S6SzImwiAt3c63352DO0iIzi4cjvLJy0EIDIumjtf/DExaYlU\nfFHC4rHPc7KiimDk7sa83Rpb9VZNLhb7oc1TAHj4yafZuHUH5eU+EhPiGHv/aGpqagC4565hWGuZ\nPvNFVq/bRMfISKY+8RC9etwAwB/efZ+ChW8CMCZ/FHcNGwrAc5mTglJzt55P5d12zqVbY+t8Bqfm\nnRrcinLdoL7cPjkQe8+bH7H5+cXcMn4kx3cc5NCyQOzcWQ+S1Ot6TpVX8v6PZuP7vJgbvzuAr48d\nTm3NWWytZeOv/puD7wcGcuMOvxra3+xYZ0/XYSG8IAtu2v8/IVnH1gYuU4AZ1trKJtu7AU9ba/+x\ntQOcG7iIiIS6cwMXJzQduIiI/C3q5PA99Bq4XB2hOnC56D0u1toWf9Naa/cZY/7HmZREREREREQa\na+3m/EWGijkAACAASURBVIv5BfDy1UpERERERORvQa0NyQmNkHfRgYsxpuUFoGCA1KufjoiIiIiI\nSHOtzbikAt8Cmj4r1ABrHclIRERERESkidYGLu8C0dbabU13GGNWOpKRiIiIiEgbZrVU7LK0dnP+\n/RfZ9/2rn46IiIiIiEhzrX0BpYiIiIiIyFdOAxcREREREQl5V/I4ZBERERER+Std5Pvf5SI0cBER\nqePkt9s/tHmKY7GdzFtE5Gqq1lofuQK6fEREREREJORpxkVEREREJIhq9Tjky6IZFxERERERCXka\nuIiIiIiISMjTUjERERERkSCyWip2WTTjIiIiIiIiIU8DFxERERERCXlaKiYiIiIiEkT6AsrLoxkX\nEREREREJeUGdcRk8eTRdBvXjzIlTvDdhHkW7DjVrk9rrevKefYB2kREc+HAbKya/AkBkbBQjXhhH\n7DXJVBwp5u2xz3PKVw1AenYfBj85GhMexo43VrJ+zjuNYoZH/P/s3Xl0FWWe//F3JRgC2fdLXMOi\nE1ljQGKDk9AQujsM2DYzDdOjRscZZGjaOQhNq/QgzWL38FNkRhQkogM6Lm3jKNiIDY4oi6xhh7Fl\n35KQmOXehLAkeX5/3CRkgyBSl1vpz+ucnJNb9Tzf+uZbVReeU09VtWPYnLEk9EyissTDsvHzcJ8o\nAqD/uOH0GpWBqa5h9bQlHPlid5vJ3e662FUTp9bbqXk7tSZ2x77W8bECwNTw62fn8MX6zURHRfLB\nmwuaxTPG8Nu5C1j75RaCg9sza8pE7ryjKwAfrljFK4vfAeCx7NHcl5XZrL9T96fybjvnplPztjt3\nJ9fFSbGlbfPZFZfOg3oTleQiJ30inzy1iMyZD7fYbuisR1j55KvkpE8kKslFUkYvwHvSHl2/j5yM\nSRxdv4+0ccMBsAIshszI5r3s2SwaMpnkEWnEdEtsFLPnqAzOllWQkz6RrYtWkvHkaABiuiWSPDyN\n1zJ/xXvZs8mc+TBWQPOnPDgxd7vrYldNnFpvp+bt1JrYHduO+AEdIgH4cVYmC+bMbDEWwNovt3Ds\nxClWvLuIaZMfZ8Zz8wAoc3uY//pbvJ0zl7dz5jL/9bcoc3sa9XXq/lTebefcdGre2p/X57vW7u9x\nf1ZjLL/+8Vc+G7h0zUxl79J1AORtP0hweAgh8ZGN2oTERxIU2oG87QcB2Lt0Hd2G9gWgW2Yqe5au\nBWDP0rX1yzv16ULpkQLKjhdSc6Ga/cs30jUztVHcbpl31ff9asVmbhnQvT6n/cs3Un2+irLjhZQe\nKaBTny5tIne762JXTZxab6fm7dSa2B3bjvgBQR0B6NunJxHhYVzKZ+s2MuKHg7Esi949kvF4yiks\nKmb9pm3c0y+FiPAwIsLDuKdfCus3bWvU16n7U3m3nXPTqXnbnbuT6+LU2NI2+WzgEuaKwn3qm/rP\nnvxiwhKiGrdJiMKTX3yxTV4xYS5vm46x4VScLgWg4nQpHWPDAQh1ReHJa7lPnVBXFO5T3jamuoZz\nnjN0iAolrGnf/GJCm/R1au5218WumtgZ26k10XHi2+PErvgEBHIlCgq/wRUfW/85IT6WgsIiCgqL\ncMXHXVwe513ekFP3p/JuO+emU/O2O3cn18WpsaVt+tYDF8uy4q+gzRjLsrZalrV1U/nXV5eZiIiI\niIhIrcvenG9ZVnTTRcBmy7JSAMsYU9xCN4wxC4GFwM8L9h5NBcjfdYjwxBhO1rYJc0XjKShp1M9T\nUEKY6+ImwzpF48n3tjlT5CYkPpKK06WExEdypsgNQHl+CWGdWu5Tpzy/hPDEaMrzi7ECA2gf1pHK\nknI8Tfu6oimv7Zvy0BB6jR6EE3O3K7adNak+X0X2ilmOq7eOk7Z1nNiZOzXVXImEuBjyT1+8klJw\nuoiEuFgS4mLZsn3XxeWFRfRL6dWor9P2p87NtnNuOn1f2pW7nbGd+l1r9/d4cEQITmD8+D4Sf9ba\nFZciYFuDn63AjUBu7e+teWlx1hQWZ03h6z9to/vIgQB0SunCOc+Z+st7dSpOl3K+vJJOKd65m91H\nDuTAKu8c7gOrc+kx8l4Aeoy8l69rl+ftPERUkouIm+MIuCGQ5OFpHFiV2yhuw753ZN3NsQ37vMtX\n5ZI8PI3AoHZE3BxHVJKLvB3eOZTbl6zGqbnbFdvOmuxZutaR9dZx0raOEztzrznf+Illl5IxMI1l\nKz/FGMPOPfsJDQ0hLjaaAf1T2bA5lzK3hzK3hw2bcxnQv/EceaftT52bbefcdPq+1P703XetL/69\nl7bLMpd5A45lWROBTOCXxpjdtcsOG2OSrnQDs299oH4DQ2Zkk5Tei6rK83w8aSH5uw8DkL1iFouz\npgDg6pnEj54fQ7vgIA6v2cnqqUsACI4M5b6Xf0F4YgxlJ4tYNu5FzpZVAN6nUnx/6gNYgQHs/v3n\nbJy3jIFPjCR/12EOrM4lsP0NDHthLAndb+NsaTnLxs+j7HghAGnjR9Dzp+mYqho+nf4Gh9fsoiVO\nzN3uuthVE6fW26l5O7Umdse+1vFvTA4FU8Mvn/kdW7bvorTUTUx0JOMefZCqqioARt0/DGMMs+a8\nzLqNW+kQHMyMpyfQI/l2AN7/6BNylrwLwJjs0dw/bCgAL6ROdfz+VN5t59x0at7an9fnu/Zax358\n1yuOuJSx5cb7/foVlP1O/o9f1vGyAxcAy7JuAl4AjgPPADuNMZ2vdAMNBy4iIn+pJmybblvshgMX\nEZG/ZJOPvumX/+FualPiT/z6/8f9T73vl3Vs9eZ8Y8wJY8zfAWuAVUBHu5MSERERERFp6IqfKmaM\nWQYMAoYAWJb1iF1JiYiIiIiINPStHodsjKk0xuyp/fgbG/IREREREWnTjJ//+KvWHofc8p3q3sci\nJ1z7dERERERERJq77MAF7+DkB0BJk+UWsMGWjERERERERJpobeDyERBqjNnRdIVlWWtsyUhERERE\npA2r0Qsor8plBy7GmEcvs+5n1z4dERERERGR5r7VzfkiIiIiIiLXQ2tTxURERERE5Boymip2VTRw\nERHxATvfbj9h23TbYtuZt4iIyLehqWIiIiIiIuL3NHARERERERG/p6liIiIiIiI+VHO9E3AoXXER\nERERERG/p4GLiIiIiIj4PU0VExERERHxIYMeh3w1dMVFRERERET8ngYuIiIiIiLi9zRVTERERETE\nh2rM9c7AmXw2cElK78XgZx7ECgxg1ztr2DR/eaP1gUHtGDZnLAk9k6gs8bBs/DzcJ4oA6D9uOL1G\nZWCqa1g9bQlHvtjts9i+iD942oN0HtSHC5Xn+HjSQgr2HGnWJqHHbWQ9/xjtgoM49NkOPp32BgDB\nESGMeGk8ETfFUXaikA/Hvcg59xnlfZ2OFdWkbcSu47T9+etn5/DF+s1ER0XywZsLmuVqjOG3cxew\n9sstBAe3Z9aUidx5R1cAPlyxilcWvwPAY9mjuS8rs1l/nT/6TmnLNfFFfO1P+/entG0+mSpmBVgM\nmZHNe9mzWTRkMskj0ojpltioTc9RGZwtqyAnfSJbF60k48nRAMR0SyR5eBqvZf6K97JnkznzYawA\nyyexfRG/86DeRCW5yEmfyCdPLSJz5sMt1nDorEdY+eSr5KRPJCrJRVJGL8D7pXB0/T5yMiZxdP0+\n0sYNV97X6VhRTdpGbCfvzx9nZbJgzswW8wRY++UWjp04xYp3FzFt8uPMeG4eAGVuD/Nff4u3c+by\nds5c5r/+FmVuT6O+On/0ndKWa+KL+Nqfvt+f0vb4ZODSqU8XSo8UUHa8kJoL1exfvpGumamN2nTL\nvIs9S9cC8NWKzdwyoDsAXTNT2b98I9Xnqyg7XkjpkQI69enik9i+iN81M5W9S9cBkLf9IMHhIYTE\nRzZqExIfSVBoB/K2HwRg79J1dBvat3bbqfXb3rN0bf1y5e37Y0U1aRux6zhxf/bt05OI8LBmf0ud\nz9ZtZMQPB2NZFr17JOPxlFNYVMz6Tdu4p18KEeFhRISHcU+/FNZv2taor84ffae05Zr4Ir72p+/3\npz+rwfLrH3/lk4FLqCsKT15x/WdPXjFhrqhmbdynvG1MdQ3nPGfoEBVKWNO++cWENuhrZ2xfxA9z\nReE+9U2jNmEJTdokROHJbzmHjrHhVJwuBaDidCkdY8OV9yXyVk18WxOnxq7jxP3ZmoLCb3DFx9Z/\nToiPpaCwiILCIlzxcReXx3mXN81J54++U9pqTXwRX/vT9/tT2h5bBi6WZY2xLGurZVlbN5V/bccm\nRERERETkL8hlb863LOuHxpiVtb9HAHOAfsAeYIIxpqClfsaYhcBCgNm3PmDK80sI6xRdvz6sUzSe\n/JJGfcrzSwhPjKY8vxgrMID2YR2pLCnH07SvK5ryBn3tjG1X/JSHhtBr9CAA8ncdIjwxhpMN2ngK\nGsf3FJQQ5mo5hzNFbkLiI6k4XUpIfCRnitzKu0neDfupJr47f5wY2+n7szUJcTHkn754JaXgdBEJ\ncbEkxMWyZfuui8sLi+iX0qtZTjp/9J3SFmtiZ/zq81Vkr5hlW+521bxhP6ftz+CIEJxAL6C8Oq1d\ncXm2we/PA3nAcGAL8MqVbiRv5yGiklxE3BxHwA2BJA9P48Cq3EZtDqzOpcfIewG4I+tujm3Y512+\nKpfk4WkEBrUj4uY4opJc5O046JPYdsXfvmQ1i7OmsDhrCl//aRvdRw4EoFNKF855ztRf9qxTcbqU\n8+WVdErxzg3tPnIgB1Zta7btHiPv5eva5crbN8eKatK2Yjt9f7YmY2Aay1Z+ijGGnXv2ExoaQlxs\nNAP6p7Jhcy5lbg9lbg8bNucyoH/jeew6f/Sd0lZrYmf8PUvXan9eh2Nc2i7LmEs/SNqyrFxjzF21\nv+8wxvRpsK7R50uZfesDBrxPjvj+1AewAgPY/fvP2ThvGQOfGEn+rsMcWJ1LYPsbGPbCWBK638bZ\n0nKWjZ9H2fFCANLGj6DnT9MxVTV8Ov0NDq/Z1Wgbdsb2RfwhM7JJSu9FVeV5Pp60kPzdhwHIXjGL\nxVlTAHD1TOJHz4+hXXAQh9fsZPXUJQAER4Zy38u/IDwxhrKTRSwb9yJnyyqU93U6VlSTthHbaftz\nwrbpAPzymd+xZfsuSkvdxERHMu7RB6mqqgJg1P3DMMYwa87LrNu4lQ7Bwcx4egI9km8H4P2PPiFn\nybsAjMkezf3DhgLwQupUn9XcKfX25XGomviuJr6Ir/1p//58fNcrjriU8WnCKL9+k8vggnf9so6t\nDVxO4J0eZgE/B7qY2g6WZe0yxvS6ZOdadQMXERGxR93AxQ4NBy4iIv5u8tE3/fI/3E2t8vOBS6af\nDlxamyqWA4QBocBiIBbAsiwXsMPe1ERERERERLwue3O+MeY3l1ieb1nWZ/akJCIiIiIi0th3eRxy\ni4MaERERERGRa621xyG3fMeq956XhGufjoiIiIhI26bHIV+dyw5c8A5OfgA0fVGABWywJSMRERER\nEZEmWhu4fASEGmOa3YhvWdYaWzISERERERFporWb8x+9zLqfXft0RERERETatprrnYBDfZeb80VE\nRERERHxCAxcREREREfF7rd3jIiIifs7Ot9tP2Dbdtth25i0i/inQr98X7zuaKnZ1dMVFRERERET8\nngYuIiIiIiLi9zRVTERERETEh/QCyqujKy4iIiIiIuL3NHARERERERG/p6liIiIiIiI+VKOZYldF\nV1xERERERMTvaeAiIiIiIiJ+TwMXERERERHxez69x2XwtAfpPKgPFyrP8fGkhRTsOdKsTUKP28h6\n/jHaBQdx6LMdfDrtDQCCI0IY8dJ4Im6Ko+xEIR+Oe5Fz7jM+iZ+U3ovBzzyIFRjArnfWsGn+8kYx\nA4PaMWzOWBJ6JlFZ4mHZ+Hm4TxQB0H/ccHqNysBU17B62hKOfLHbZ3WxM2+nxla9Ve8rzVt1aZz3\nr5+dwxfrNxMdFckHby5olqsxht/OXcDaL7cQHNyeWVMmcucdXQH4cMUqXln8DgCPZY/mvqzMZv2d\nWBO783ZqbCfX26nftU6q+UfjXuRcmbfmt6X3YtA0b9573lnD5peb5/2jF8YS3zOJsyUePvq5N+/g\nyFCGL3gcV+/O7H3vC/536pJm+fi7Gj0O+ar47IpL50G9iUpykZM+kU+eWkTmzIdbbDd01iOsfPJV\nctInEpXkIimjF+A9sY6u30dOxiSOrt9H2rjhPolvBVgMmZHNe9mzWTRkMskj0ojpltgoZs9RGZwt\nqyAnfSJbF60k48nRAMR0SyR5eBqvZf6K97JnkznzYayAxgeqE/N2amzVW/W+0rxVl+Z5/zgrkwVz\nZraYJ8DaL7dw7MQpVry7iGmTH2fGc/MAKHN7mP/6W7ydM5e3c+Yy//W3KHN7mvV3Yk3szNupsZ1c\nb6d+1zqt5nc3OFYGz8zm/ezZ/NfgydwxIo3oJnn3qM37tb+eyLZXV/LXT3nzrjp3gQ3P/4HPZ73V\nYi7Sdvls4NI1M5W9S9cBkLf9IMHhIYTERzZqExIfSVBoB/K2HwRg79J1dBvaF4BumansWboWgD1L\n19Yvtzt+pz5dKD1SQNnxQmouVLN/+Ua6ZqY2itst8676vl+t2MwtA7rX57R/+Uaqz1dRdryQ0iMF\ndOrTxfF5OzW26q16X2neqkvzvPv26UlEeBiX8tm6jYz44WAsy6J3j2Q8nnIKi4pZv2kb9/RLISI8\njIjwMO7pl8L6Tdua9XdiTezM26mxnVxvp37XOq3mXWuXu+ryPubN+6vlG+k6tHHeXYfexd4/ePv+\nuUHeVZXnOLnlz1SfvYD8ZfHZwCXMFYX71Df1nz35xYQlRDVukxCFJ7/4Ypu8YsJc3jYdY8OpOF0K\nQMXpUjrGhvskfqgrCk9ey33qhLqicJ/ytjHVNZzznKFDVChhTfvmFxPapK8T83ZqbFC9Ve8ry1t1\naTnvyyko/AZXfGz954T4WAoKiygoLMIVH3dxeZx3eVNOrYmd/7Y5MbaT6+3U71qn1jzUFYXnVOM+\noQktfBe3kHdbYPz8x19964GLZVkxV9BmjGVZWy3L2rqp/Oury0xERERERKTWZW/Otyzrd8Bzxpgi\ny7L6Ar8HaizLugF4yBjzeUv9jDELgYXAzwv2Hk0FyN91iPDEGE7WtglzReMpKGnUz1NQQpgruv5z\nWKdoPPneNmeK3ITER1JxupSQ+EjOFLlJeWgIvUYPwq74AOX5JYR1arlPnfL8EsIToynPL8YKDKB9\nWEcqS8rxNO3riqY8v8SxeTfs56TYqrfq/W1qorpcui6XkxAXQ/7pi1dSCk4XkRAXS0JcLFu277q4\nvLCIfineue9OrYmdeVefryJ7xSzHxXZqvevytit3p8b22bmZ2LhPeUEL38Ut5C1/uVq74jLMGFP3\nL9H/A0YZY7oCmcDzVxD/pcVZU1icNYWv/7SN7iMHAtAppQvnPGfqLx3WqThdyvnySjqleOdXdh85\nkAOrvHOhD6zOpcfIewHoMfJevl61je1LVmNnfIC8nYeISnIRcXMcATcEkjw8jQOrchvFbdj3jqy7\nObZhn3f5qlySh6cRGNSOiJvjiEpykbfjoGPzruO02Kq36v1taqK6XLoul5MxMI1lKz/FGMPOPfsJ\nDQ0hLjaaAf1T2bA5lzK3hzK3hw2bcxnQ3zuP3ak1sTPvPUvXOjK2U+tdl7dduTs1tp01P1i7PH/n\nISKTXITX5n3H8DQONsn74Kpcuv+tt+/tDfJuC2r8/MdfWcZceiabZVn7gZ7GmCrLsjYaY9IarNtt\njOnZ2gZm3/pA/QaGzMgmKb0XVZXn+XjSQvJ3HwYge8UsFmdNAcDVM4kfPT+GdsFBHF6zk9W1j7gL\njgzlvpd/QXhiDGUni1g27kXOllU02pZd8TsP6s33pz6AFRjA7t9/zsZ5yxj4xEjydx3mwOpcAtvf\nwLAXxpLQ/TbOlpazbPw8yo4XApA2fgQ9f5qOqarh0+lvcHjNLppyYt5Oja16q95Xmrfq4s17wrbp\nAPzymd+xZfsuSkvdxERHMu7RB6mqqgJg1P3DMMYwa87LrNu4lQ7Bwcx4egI9km8H4P2PPiFnybsA\njMkezf3DhgLwQupUR9bEV/vSqbGdXG+nftc6qeZ//JeLNU8a1JuMZx4gIDCAPe9+zqZ5y/jeEyMp\n2H2Yg6u8ef9o7ljia/P+4/h5lB3z5v1P618gKKwDgTe045z7DH944HcUf32KicfedMRzht93/cyf\nbyXhJ/lv+WUdWxu4/AIYDvwO+GsgCngf+D7Q2RjzYGsbaDhwERERZ6kbuNih6cBFRNq+QJv/V6iB\ny7XhrwOXy97jYox50bKs3cC/ALfXtu8GfADMsD89EREREZG2pcbyy3GB37vswAXAGLMGWNN0uWVZ\njwCvX/uUREREREREGvsu73H5zTXLQkRERERE5DJaexxy8zu0alcBCdc+HRERERGRts2vb3DxY61N\nFUsAfgA0fZi/BWywJSMREREREZEmWhu4fASEGmN2NF1hWdYaWzISERERERFporWnij16mXU/u/bp\niIiIiIi0bf78kkd/9l1uzhcREREREfEJDVxERERERMTvtfoeFxER+ctl59vtJ2ybbltsO/MWkavX\nXo/Tku9AAxcRERERER+qsa53Bs6kqWIiIiIiIuL3NHARERERERG/p6liIiIiIiI+VIPmil0NXXER\nERERERG/p4GLiIiIiIj4PU0VExERERHxIT0V+uroiouIiIiIiPg9DVxERERERMTv+WyqWFJ6LwY/\n8yBWYAC73lnDpvnLG60PDGrHsDljSeiZRGWJh2Xj5+E+UQRA/3HD6TUqA1Ndw+ppSzjyxW6fxfZF\n/MHTHqTzoD5cqDzHx5MWUrDnSLM2CT1uI+v5x2gXHMShz3bw6bQ3AAiOCGHES+OJuCmOshOFfDju\nRc65zzi65qq36n296+303O3K+1rHxgoAU8Ovn53DF+s3Ex0VyQdvLmgWzxjDb+cuYO2XWwgObs+s\nKRO5846uAHy4YhWvLH4HgMeyR3NfVmaz/k6qiWJf39h2xnfq97idsW/J6MW907yx9729htyXG8cO\nCGpH5tyxxPVM4myJh0/GzcNTGxsgNDGGn/3vv7PlhffZ/sqKZvvJn+kFlFfHJ1dcrACLITOyeS97\nNouGTCZ5RBox3RIbtek5KoOzZRXkpE9k66KVZDw5GoCYbokkD0/jtcxf8V72bDJnPowVYPkkti/i\ndx7Um6gkFznpE/nkqUVkzny4xRoOnfUIK598lZz0iUQluUjK6AV4vxSOrt9HTsYkjq7fR9q44Y6u\nueqtel/vejs9d7vytiN2QIdIAH6clcmCOTNbjAWw9sstHDtxihXvLmLa5MeZ8dw8AMrcHua//hZv\n58zl7Zy5zH/9Lcrcnmb9nVQTxb5+se2M79Tvcbtjp8/MZvlDs3nr+5O5/b40oprEvnN0BudKK3jz\n3onsfHUl33t6dKP1A6f+A8c+29niPpK2yScDl059ulB6pICy44XUXKhm//KNdM1MbdSmW+Zd7Fm6\nFoCvVmzmlgHdAeiamcr+5RupPl9F2fFCSo8U0KlPF5/E9kX8rpmp7F26DoC87QcJDg8hJD6yUZuQ\n+EiCQjuQt/0gAHuXrqPb0L61206t3/aepWvrlzu15qq36n296+303O3K247YAUEdAejbpycR4WHN\n9kGdz9ZtZMQPB2NZFr17JOPxlFNYVMz6Tdu4p18KEeFhRISHcU+/FNZv2tasv5NqotjXL7ad8Z36\nPW5n7IQ+XSg7UoD7mDf218s20nlo49idh97F//3BG/vAHzdzU21sgKQfpOI+Xkjxn08ifzl8MnAJ\ndUXhySuu/+zJKybMFdWsjfuUt42pruGc5wwdokIJa9o3v5jQBn3tjO2L+GGuKNynvmnUJiyhSZuE\nKDz5LefQMTacitOlAFScLqVjbLjteTs1NqjeqveV5e3k3O3K247YBARyJQoKv8EVH1v/OSE+loLC\nIgoKi3DFx11cHudd3pSTaqLY1y+2nfGd+j1uZ+wQVxSeUxfXl+cVE9IkdsM2prqG854zBEeFckPH\n9qT+y9+w5YX3caoaP//xV7YMXCzLGmNZ1lbLsrZuKv/ajk2IiIiIyF+gu5/4CTteXcmFM+eudyri\nY5e9Od+yrFzgfeBtY8zBKw1qjFkILASYfesDpjy/hLBO0fXrwzpF48kvadSnPL+E8MRoyvOLsQID\naB/WkcqScjxN+7qiKW/Q187YdsVPeWgIvUYPAiB/1yHCE2M42aCNp6BxfE9BCWGulnM4U+QmJD6S\nitOlhMRHcqbIbXtdnBZb9Va9r7QmTs3dzryrz1eRvWKWLbGpqeZKJMTFkH/64pWUgtNFJMTFkhAX\ny5btuy4uLyyiX4r3XgOn1kSxfRv7TJHbsed9w35OjF2RX0JY4sX1oZ2iqWgSu65NRW3soLCOnC0p\nJyGlK12y7uZ7T4+mfXhHjDFUnb3A7sWrkLattSsuUUAk8JllWZsty5pgWVZiK32aydt5iKgkFxE3\nxxFwQyDJw9M4sCq3UZsDq3PpMfJeAO7IuptjG/Z5l6/KJXl4GoFB7Yi4OY6oJBd5Ow76JLZd8bcv\nWc3irCkszprC13/aRveRAwHolNKFc54z9ZeZ61ScLuV8eSWdUrxzQ7uPHMiBVduabbvHyHv5una5\nU2uueqve17PeTs3dzrz3LF1rW+ya842f5nQpGQPTWLbyU4wx7Nyzn9DQEOJioxnQP5UNm3Mpc3so\nc3vYsDmXAf1Tbd+XdtZEsX0b++tV2xx73tdxauyCnYeIuM1FWG3sbiPSONwk9uFVufzV33pjdx12\nNyfWe2O/P3IGS743gSXfm8DORZ+wbd4yDVr+QljGXPrdnZZl5Rpj7qr9/V7g74GfAPvxXoVZ2NoG\nZt/6gAHvkzq+P/UBrMAAdv/+czbOW8bAJ0aSv+swB1bnEtj+Boa9MJaE7rdxtrScZePnUXa8EIC0\ngT3ZxgAAIABJREFU8SPo+dN0TFUNn05/g8NrdjXahp2xfRF/yIxsktJ7UVV5no8nLSR/92EAslfM\nYnHWFABcPZP40fNjaBccxOE1O1k9dQkAwZGh3PfyLwhPjKHsZBHLxr3I2bIK2/N2amzVW/W+0ryd\nnLtdeV/r2Dcmh4Kp4ZfP/I4t23dRWuomJjqScY8+SFVVFQCj7h+GMYZZc15m3catdAgOZsbTE+iR\nfDsA73/0CTlL3gVgTPZo7h82FIAXUqc6siaKfX1j2xnfqd/j1zp2xwY3UNw6qDf3TvPG3vfu52x7\ncRl3TxzJ6V2HObLKGztz7lhie9zGudJyPvn5PNzHChvld/eEn3DhzNn6xyGPP/6mIx40/PqND1z6\nP+B+4JGT/lnHKx64NFgWCGQCo4wxj7S2gbqBi4iISEMTtk23LXbTgYuI+IeONt/5rYHLteGvA5fW\nXkD556YLjDHVwMraHxEREREREdtdduBijBl9qXWWZT1ijHn92qckIiIiItJ21fjl9Qz/910eh/yb\na5aFiIiIiIjIZbT2OOSW71gFC0i49umIiIiIiIg019o9LgnAD4CSJsstYIMtGYmIiIiItGH+/HZ6\nf9bawOUjINQYs6PpCsuy1tiSkYiIiIiISBOt3Zz/6GXW/ezapyMiIiIiItJca1dcRERERETkGtJU\nsavzXZ4qJiIiIiIi4hO64iIiIteFnW+3n7Btum2x7cxbpK3LC9S1Brl6GriIiIiIiPiQ0Qsor4qm\niomIiIiIiN/TwEVERERERPyeBi4iIiIiIuL3dI+LiIiIiIgP6REFV0dXXERERERExO9p4CIiIiIi\nIn5PU8VERERERHxIU8Wujq64iIiIiIiI3/PZFZek9F4MfuZBrMAAdr2zhk3zlzdaHxjUjmFzxpLQ\nM4nKEg/Lxs/DfaIIgP7jhtNrVAamuobV05Zw5IvdPovti/iDpz1I50F9uFB5jo8nLaRgz5FmbRJ6\n3EbW84/RLjiIQ5/t4NNpbwAQHBHCiJfGE3FTHGUnCvlw3Iucc5+xPbZq4tuaOLXeTs3bybnrOPTW\nBCsATA2/fnYOX6zfTHRUJB+8uaBZPGMMv527gLVfbiE4uD2zpkzkzju6AvDhilW8svgdAB7LHs19\nWZnN+qveOu/b+v681rHf+Pl/ctZdUd9v2DMPccegPlyoPM/SSQs4tbd57MQeSYx87jFuCA7iq892\n8MffLKlfl5Y9lLSHhlJTXcNX/7u9WV9pW3xyxcUKsBgyI5v3smezaMhkkkekEdMtsVGbnqMyOFtW\nQU76RLYuWknGk6MBiOmWSPLwNF7L/BXvZc8mc+bDWAGWT2L7In7nQb2JSnKRkz6RT55aRObMh1us\n4dBZj7DyyVfJSZ9IVJKLpIxegPfL7Oj6feRkTOLo+n2kjRtue2zVxLc1cWq9nZq3k3PXcXixJgEd\nIgH4cVYmC+bMbDEWwNovt3DsxClWvLuIaZMfZ8Zz8wAoc3uY//pbvJ0zl7dz5jL/9bcoc3sa9VW9\ndd639f1pR+z0BrFvz+hDbJKLORlP8MHTrzJi1j+2GPu+mf/IB0+9ypyMJ4hNcnF7Rm8Aku65k+TM\nvrz4oyf5z6GTWZfzxxb7+yPj5z/+yicDl059ulB6pICy44XUXKhm//KNdM1MbdSmW+Zd7Fm6FoCv\nVmzmlgHdAeiamcr+5RupPl9F2fFCSo8U0KlPF5/E9kX8rpmp7F26DoC87QcJDg8hJD6yUZuQ+EiC\nQjuQt/0gAHuXrqPb0L61206t3/aepWvrl9sZWzXxbU2cWm+n5u3k3HUcXqxJQFBHAPr26UlEeBiX\n8tm6jYz44WAsy6J3j2Q8nnIKi4pZv2kb9/RLISI8jIjwMO7pl8L6Tdsa9VW9dd639f1pR+zkzIux\nk4emsv1977rj2w8QHNaRsLjGscPiImkf1oHj2w8AsP39tSTXxu7/D0P4Yv4yqs9XAVDxjRtp23wy\ncAl1ReHJK67/7MkrJswV1ayN+5S3jamu4ZznDB2iQglr2je/mNAGfe2M7Yv4Ya4o3Ke+adQmLKFJ\nm4QoPPkt59AxNpyK06UAVJwupWNsuO2xVRPf1sSp9XZq3k7OXcfhxZoQEMiVKCj8Bld8bP3nhPhY\nCgqLKCgswhUfd3F5nHd5079X9dZ535b3px2xQ+Mi6tuFJ0RRdupiP3d+MeFN/rZwVxRlDf7+srxi\nwmu3H9vZxW1338HYD6bzT+/+Gzf26oy0bXqqmIiIiIj4xjWchxQQGEiHiFAW/HgqN/XuwuiXHgew\nru1W7FFjtd5GmrvswMWyrL7A/wNOAk8BrwF3A38GxhhjWrwLyrKsMcAYgJ9E301iflfCOkXXrw/r\nFI0nv6RRn/L8EsIToynPL8YKDKB9WEcqS8rx5Jc07uuKprxB3/Km669hbLvipzw0hF6jBwGQv+sQ\n4YkxnGzQxlPQOL6noIQwV8s5nClyExIfScXpUkLiI6k+X0X2ilm2xD5T5FZNfFiThv2cVG+nHid1\neTs5dx2HF2tCTTVXIiEuhvzTF6+kFJwuIiEuloS4WLZs33VxeWER/VJ6Nft7VW+d921xf9r572bV\n+QuMX/EsACd2HiIi8WK/cFc07iZ1ceeXENHg74/oFI27dvtl+cXs/WRLbayDmBoDEAsUIm1Sa1PF\nXgZmA38ENgCvGGMigCdr17XIGLPQGNPXGNO3f2g38nYeIirJRcTNcQTcEEjy8DQOrMpt1OfA6lx6\njLwXgDuy7ubYhn3e5atySR6eRmBQOyJujiMqyUXejoP1/eyMbVf87UtWszhrCouzpvD1n7bRfeRA\nADqldOGc50z9JdU6FadLOV9eSacU75zW7iMHcmDVtmbb7jHyXvYsXWtb7K9rl6smvqlJHafV26nH\nSV3eTs5dx+HFmtScP8OVyBiYxrKVn2KMYeee/YSGhhAXG82A/qls2JxLmdtDmdvDhs25DOjf+J4E\n1VvnfVvdn3b+u7l96VrmZT3NvKyn2f+nraT8xLvu5pSunPNU4ilsHNtTWMo5TyU3p3if9pfyk3vZ\n/ydv7P1/2krntDsBiElyEXhDO4DGczqlTbGMufTVNMuythtjUmp/P2aMuaWldZcz+9YHDHifSvH9\nqQ9gBQaw+/efs3HeMgY+MZL8XYc5sDqXwPY3MOyFsSR0v42zpeUsGz+PsuPeAXPa+BH0/Gk6pqqG\nT6e/weE1uxptw87Yvog/ZEY2Sem9qKo8z8eTFpK/+zAA2StmsThrCgCunkn86PkxtAsO4vCanaye\n6n0UYHBkKPe9/AvCE2MoO1nEsnEvcraswvbYqolva+LUejs1byfnruPQW5Mbk0PB1PDLZ37Hlu27\nKC11ExMdybhHH6Sqynsj76j7h2GMYdacl1m3cSsdgoOZ8fQEeiTfDsD7H31CzpJ3ARiTPZr7hw0F\n4IXUqT6piZPqrfO+7e7Pax37zZ//B5UNYg+f/jDd0ntzofIc7//yFU7Wxh6/4lnmZT0NwI09kxj5\n3FjaBQfx9ZqdLH/mvwAIvCGQn8x+jE533kr1hSo+nvXfPPr2rx0xCeuFWx7w6+lsE4696Zd1bG3g\n8iXwDBABPAf8qzHmA8uy0oHnjTF9L9m5Vt3ARURExFcmbJtuW+yGAxcR+XbKLHvfGT/ryFt++R/u\npjRwuTqt3Zw/Fu9UsRrgB8C/WJb1X3jveflne1MTERERERHxuuzAxRizE++Apc6/1v5gWdYjeO97\nERERERGRK2Tvdae267u8x+U31ywLERERERGRy2jtccjN7/6qXQUkXPt0REREREREmmvtHpcEvFPF\nSpost9A0MRERERER8ZHWpop9BIQaY442+TkCrLE9OxERERGRNsb4+c+VsCzrh5ZlfWVZ1gHLsp5s\nYX17y7LerV2/ybKs264w9CVdduBijHnUGLPuEut+9l03LiIiIiIizmJZViDwEvAj4E7g7y3LurNJ\ns0eBEmNMV+AF4N+/63a/y835IiIiIiLyl+du4IAx5pAx5jzwDnBfkzb3AYtrf/8DMNiyrO/0fpjW\n7nEREREREZFrqMYvX+94kWVZY4AxDRYtNMYsbPD5RuB4g88ngP5NwtS3McZUWZZVBsQARVeblwYu\nIiLS5tj5dvsJ26bbFhvszV3kehsTffp6pyBXoHaQsrDVhj6mqWIiIiJ+QoMWEXGIk8DNDT7fVLus\nxTaWZbUDIoBvvstGNXAREREREfGhGj//uQJbgG6WZSVZlhUEjAaWNWmzDMiu/f1vgf81xlzpQ8ta\npKliIiIiIiJyxWrvWRkPfAIEAq8ZY/ZaljUd2GqMWQYsAt6wLOsAUIx3cPOdaOAiIiIiIiLfijFm\nBbCiybKpDX4/C/zdtdymBi4iIiIiIj70neZL/QXTPS4iIiIiIuL3NHARERERERG/p6liIiIiIiI+\nVKPJYldFV1xERERERMTv+eyKS1J6LwY/8yBWYAC73lnDpvnLG60PDGrHsDljSeiZRGWJh2Xj5+E+\nUQRA/3HD6TUqA1Ndw+ppSzjyxW6fxfZF/MHTHqTzoD5cqDzHx5MWUrDnSLM2CT1uI+v5x2gXHMSh\nz3bw6bQ3AAiOCGHES+OJuCmOshOFfDjuRc65z9ge26n7U/tSx8mV7kvVpe0c43bW5NfPzuGL9ZuJ\njorkgzcXNMvXGMNv5y5g7ZdbCA5uz6wpE7nzjq4AfLhiFa8sfgeAx7JHc19Wps/q7dRj0MnnvWJ7\nYwf8+t+o8ZR7132vH9GTxkFgAOX/8zHu/3qnUczQkX9D2E/vg5pqas6cpXjmHC4cPla/PtAVT+If\nFlH2yhLcb7zXLCdpe3xyxcUKsBgyI5v3smezaMhkkkekEdMtsVGbnqMyOFtWQU76RLYuWknGk95H\nPcd0SyR5eBqvZf6K97JnkznzYawAyyexfRG/86DeRCW5yEmfyCdPLSJz5sMt1nDorEdY+eSr5KRP\nJCrJRVJGL8D7RXx0/T5yMiZxdP0+0sYNtz22U/en9qWOkyuJrbq0rWPc7pr8OCuTBXNmtpgrwNov\nt3DsxClWvLuIaZMfZ8Zz8wAoc3uY//pbvJ0zl7dz5jL/9bcoc3t8Um+nHoNOPu8V+2Ls8EdqX+UR\nEED0r37B6V88zamRjxLyw0HckHRLo5gVK/+XvFH/TN7fj8W9+F2iJv5Lo/VRT4ylcv3mFvORtskn\nA5dOfbpQeqSAsuOF1FyoZv/yjXTNTG3UplvmXexZuhaAr1Zs5pYB3QHompnK/uUbqT5fRdnxQkqP\nFNCpTxefxPZF/K6Zqexdug6AvO0HCQ4PISQ+slGbkPhIgkI7kLf9IAB7l66j29C+tdtOrd/2nqVr\n65fbGdup+1P7UsfJlcRWXdrWMW53Tfr26UlEeBiX8tm6jYz44WAsy6J3j2Q8nnIKi4pZv2kb9/RL\nISI8jIjwMO7pl8L6Tdt8Um+nHoNOPu8V+2LsjhkDAAjqcQdVJ05RdTIPqqqo+GQNHWrX1TEVF6/6\nWB2CocFL1ztkfI+qU/lcOHQUJ7LzrffX4sdf+WTgEuqKwpNXXP/Zk1dMmCuqWRv3KW8bU13DOc8Z\nOkSFEta0b34xoQ362hnbF/HDXFG4T33TqE1YQpM2CVF48lvOoWNsOBWnSwGoOF1Kx9hw22M7dX9q\nX+o4udJ9qbq0nWPcF8fK5RQUfoMrPrb+c0J8LAWFRRQUFuGKj7u4PM67vNHf68CaODV2HSce406L\nHRjjXd4uLpaq/NP1fapPFxIYH0NToT8dQeKHS4j613+mePZLgHcQE/HwaMpeWdKsvbRtujlfRERE\nRHzCmG/3NK3y3y/j1H0PUfKfrxLxT/8AQMRjD+H+76WYyrN2pCh+7LI351uWFQpMBkYCNwHngYPA\nAmPMf12m3xhgDMBPou8mMb8rYZ2i69eHdYrGk1/SqE95fgnhidGU5xdjBQbQPqwjlSXlePJLGvd1\nRVPeoG950/XXMLZd8VMeGkKv0YMAyN91iPDEGE42aOMpaBzfU1BCmKvlHM4UuQmJj6TidCkh8ZFU\nn68ie8UsW2KfKXLbXnOnxXbqvtRx0nJsO/enU+vi1GPcF/W+EglxMeSfvnglpeB0EQlxsSTExbJl\n+66LywuL6JfSiyQdgz6P7dRj3Kmxa4q9V1+qCoto54qv7xMYH0f16W+4lDOffEbMU//KN0D7nsmE\nDPlrov71nwkIC8XU1GDOn8fz7oeX7O9v9DDkq9PaFZf/Bg4BPwB+A/wn8CAwyLKsZy/VyRiz0BjT\n1xjTt39oN/J2HiIqyUXEzXEE3BBI8vA0DqzKbdTnwOpceoy8F4A7su7m2IZ93uWrckkenkZgUDsi\nbo4jKslF3o6D9f3sjG1X/O1LVrM4awqLs6bw9Z+20X3kQAA6pXThnOdM/SXVOhWnSzlfXkmnFO98\n3O4jB3Jg1bZm2+4x8l72LF1rW+yva5c7dX9qX+o4aS22nfvTqXVx6jHui3pfiYyBaSxb+SnGGHbu\n2U9oaAhxsdEM6J/Khs25lLk9lLk9bNicy4D+qToG29h579TvcTtjn/l8AwDn935Fu5tvpF2iC9q1\nI+QHGVTWrqvT7uYb63/vcG9/Lhw/AUDBoxM4+TcPcPJvHsD91vu4X3vbUYMWuXrW5S7ZWZa10xjT\nu8HnLcaYfpZlBQD7jDF/1doGZt/6gAHvUym+P/UBrMAAdv/+czbOW8bAJ0aSv+swB1bnEtj+Boa9\nMJaE7rdxtrScZePnUXa8EIC08SPo+dN0TFUNn05/g8NrdjXahp2xfRF/yIxsktJ7UVV5no8nLSR/\n92EAslfMYnHWFABcPZP40fNjaBccxOE1O1k91TuvMzgylPte/gXhiTGUnSxi2bgXOVtWYXtsp+5P\n7UsdJ1e6L1WXtnOMX+uaTNg2vT7nXz7zO7Zs30VpqZuY6EjGPfogVVVVAIy6fxjGGGbNeZl1G7fS\nITiYGU9PoEfy7QC8/9En5Cx5F4Ax2aO5f9hQXkidqmOwjZ73iu2NHfjrf6Om9gl6wQPu9j4OOSCA\n8mUrcS96i4ix2Zzf92cqv/iSqEnjCO5/F1RVUeMup/jfX2x2M37EYw9hzlTWPw751tzVzR8X54em\n3/oPfn3RZerR//bLOrY2cNkATDbGrLMsawTwc2PMD2rXfWWMuaO1DdQNXERERNqChgOXa63pwEWk\nrRkVk29rfKcMXKb5+cBlmp8OXFp7AeVY4FXLsroBe4F/BLAsKw54yebcREREREREgFYGLsaYXcDd\nLSwvtCzL00IXERERERGRa661Ky6X8xvg9WuViIiIiIjIX4Iav5yI5f9aexxyy3eugQUkXPt0RERE\nREREmmvtiksC3kchN31wvQVsaN5cRERERETk2mtt4PIREGqM2dF0hWVZa2zJSERERESkDavRKyiv\nSms35z96mXU/u/bpiIiIiIiINBdwvRMQERERERFpzXd5qpiIiIiIiHxLmih2dTRwERER+Rbsfrv9\nhG3TbYttd+4irdmYZ+9DaW+1Nbpcb5oqJiIi4ifsHLSIiDidBi4iIiIiIuL3NFVMRERERMSHaq53\nAg6lKy4iIiIiIuL3NHARERERERG/p6liIiIiIiI+VKMHIl8VXXERERERERG/p4GLiIiIiIj4PU0V\nExERERHxIU0Uuzq64iIiIiIiIn7Pp1dcBk97kM6D+nCh8hwfT1pIwZ4jzdok9LiNrOcfo11wEIc+\n28Gn094AIDgihBEvjSfipjjKThTy4bgXOec+A0BSei8GP/MgVmAAu95Zw6b5yxvFDAxqx7A5Y0no\nmURliYdl4+fhPlEEQP9xw+k1KgNTXcPqaUs48sVun+au2L6NbeexothtJ3YdJx6Hdubt1NhOqjdW\nABjvGx5+/ewcvli/meioSD54c0GzmMYYfjt3AWu/3EJwcHtmTZnInXd0BeDDFat4ZfE7ADyWPZr7\nsjIb9XXquenk896pse2suWtQL1Kme2MfemsN/zevcey4tL8iZfoDRCTfwpdj53Hij5vr1/31W5OJ\nuasrRZv/zNqHnmv2t0rb5LMrLp0H9SYqyUVO+kQ+eWoRmTMfbrHd0FmPsPLJV8lJn0hUkoukjF6A\n9+A/un4fORmTOLp+H2njhgNgBVgMmZHNe9mzWTRkMskj0ojpltgoZs9RGZwtqyAnfSJbF60k48nR\nAMR0SyR5eBqvZf6K97JnkznzYawAy2e5K7ZvY9t5rCh224ldx4nHoZ15OzW20+od0CGyvs+PszJZ\nMGdmi/EA1n65hWMnTrHi3UVMm/w4M56bB0CZ28P819/i7Zy5vJ0zl/mvv0WZ21Pfz6nnppPPe6fG\ntnt/pj77MF/8w2xWpk/m1h/fQ/jtNzaKXXGiiE3/+grH/mdDs7/l/17+I5t+Mb/Fv9MJavz8x1/5\nbODSNTOVvUvXAZC3/SDB4SGExEc2ahMSH0lQaAfyth8EYO/SdXQb2heAbpmp7Fm6FoA9S9fWL+/U\npwulRwooO15IzYVq9i/fSNfM1EZxu2XeVd/3qxWbuWVA9/qc9i/fSPX5KsqOF1J6pIBOfbr4LHfF\n9m1sO48VxW47ses48Ti0M2+nxnZavQOCOtb369unJxHhYVzKZ+s2MuKHg7Esi949kvF4yiksKmb9\npm3c0y+FiPAwIsLDuKdfCus3bavv59Rz08nnvVNj21nz6JQueI4UUHHMG/vYhxu58QeNY585UUTZ\n/uOYmuZ3hJxet5cL5WebLZe27bIDF8uyIizL+p1lWf9nWVaxZVnfWJa1v3ZZ5OX6NhXmisJ96pv6\nz578YsISohq3SYjCk198sU1eMWEub5uOseFUnC4FoOJ0KR1jwwEIdUXhyWu5T51QVxTuU942prqG\nc54zdIgKJaxp3/xiQpv0tTN3xfZtbDuPFcVuO7HrOPE4tDNvp8Z2Wr0JCORKFRR+gys+tv5zQnws\nBYVFFBQW4YqPu7g8zru84d/rxHPTyee9U2PbWfMOrmgqT17M+UxeMR1a2CciDbV2xeX3QAmQYYyJ\nNsbEAINql/3+Up0syxpjWdZWy7K2bir/+tplKyIiIiLicDUYv/7xV63dnH+bMebfGy4wxuQD/25Z\n1j9eqpMxZiGwEPh5wd6jqQD5uw4RnhjDydo2Ya5oPAUljfp5CkoIc0XXfw7rFI0n39vmTJGbkPhI\nKk6XEhIfyZkiNwDl+SWEdWq5T53y/BLCE6Mpzy/GCgygfVhHKkvK8TTt64qmvLZvykND6DV6EHbk\nXn2+iuwVsxTbR7HtPlYUu+3EtvO8t/M41PdV26k3NdVcqYS4GPJPX7ySUnC6iIS4WBLiYtmyfdfF\n5YVF9Evp1ejvddq5aWdsnT++PX9uqP1cmV9Mhxtj6td37BRNZZPYIk21dsXlqGVZky3LSqhbYFlW\ngmVZvwKOX0H8lxZnTWFx1hS+/tM2uo8cCECnlC6c85ypvyxZp+J0KefLK+mU4p0D2X3kQA6s8s7L\nPbA6lx4j7wWgx8h7+bp2ed7OQ0QluYi4OY6AGwJJHp7GgVW5jeI27HtH1t0c27DPu3xVLsnD0wgM\nakfEzXFEJbnI2+Gd+7l9yWrsyn3P0rWK7cPYdh8rit12Ytt53tt5HOr7qu3Uu+b8Ga5UxsA0lq38\nFGMMO/fsJzQ0hLjYaAb0T2XD5lzK3B7K3B42bM5lQP+L9w448dy0M7bOn+vz72bxjkOEJbkIqY19\ny31pnPxkGyKXYxlz6ctBlmVFAU8C9wEJeN+XUwAsA/7dGFN8yc61Zt/6QP0GhszIJim9F1WV5/l4\n0kLydx8GIHvFLBZnTQHA1TOJHz0/hnbBQRxes5PVU5cAEBwZyn0v/4LwxBjKThaxbNyLnC2rALxP\n0/j+1AewAgPY/fvP2ThvGQOfGEn+rsMcWJ1LYPsbGPbCWBK638bZ0nKWjZ9H2fFCANLGj6DnT9Mx\nVTV8Ov0NDq/ZRUvsyl2xfRvbzmNFsdtObCcfh3bm7dTYTqr3jcmh9Y9D/uUzv2PL9l2UlrqJiY5k\n3KMPUlVVBcCo+4dhjGHWnJdZt3ErHYKDmfH0BHok3w7A+x99Qs6SdwEYkz2a+4cNBeCF1Km218Sp\nsZ18jNsZ+1rX/NbzF//f2en7vS8+Dvmdz9n/Hx/S45cjKd55mFN/yiW6d2cGvDaBoMiOVJ+9wNnC\nMlZm/AqA73/wb4R1TaRdx2DOl5SzZeJC8tfsZlTefzd/XJwfeuK20f47HwuYc+Qdv6zjZQcuAJZl\n/RVwE7DRGFPeYPkPjTErW9tAw4GLiIiIXNqEbdNtjV83cBG5XhoOXOzglIHLBD8fuLzgpwOX1p4q\n9jjwITAe2GNZ1n0NVj9rZ2IiIiIiIiJ1Wrs5/5+BVGNMuWVZtwF/sCzrNmPMfwB+ORITEREREZG2\np7WBS0Dd9DBjzBHLsjLwDl5uRQMXEREREZFvzZ/fTu/PWnuqWIFlWX3qPtQOYv4GiAV62pmYiIiI\niIhIndYGLg8B+Q0XGGOqjDEPAX9tW1YiIiIiIiINXHaqmDHmxGXWrb/26YiIiIiItG3Gj99O789a\nu+IiIiIiIiJy3WngIiIiIiIifq+1p4qJiIiIj9j9gkg7X3Cpl1vKldjR3uYXUNoa/drRU8Wujq64\niIiIiIiI39PARURERERE/J6miomIiIiI+FCNnip2VXTFRURERERE/J4GLiIiIiIi4vc0cBERERER\nEb+ne1xERERERHxId7hcHV1xERERERERv6eBi4iIiIiI+D1NFRMRERER8SE9Dvnq+GzgkpTei8HP\nPIgVGMCud9awaf7yRusDg9oxbM5YEnomUVniYdn4ebhPFAHQf9xweo3KwFTXsHraEo58sdtnsesM\nnvYgnQf14ULlOT6etJCCPUeatUnocRtZzz9Gu+AgDn22g0+nvQFAcEQII14aT8RNcZSdKOTDcS9y\nzn3G8XWxqyZOrbdq0nZq4uS6ODVvp8Z26nf4r5+dwxfrNxMdFckHby5oVgtjDL+du4C1X24eLLPW\nAAAgAElEQVQhOLg9s6ZM5M47ugLw4YpVvLL4HQAeyx7NfVmZPsvbyf/e2xnbSef94p//J2fdFfX9\nhj/zEHcM6sP5yvP8YdICTu1tHjuxRxJ/99xj3BAcxFef7WD5b5bUr7sneyhpDw3FVNfwf/+7vVlf\naVt8MlXMCrAYMiOb97Jns2jIZJJHpBHTLbFRm56jMjhbVkFO+kS2LlpJxpOjAYjplkjy8DRey/wV\n72XPJnPmw1gBlk9i1+k8qDdRSS5y0ifyyVOLyJz5cIt/59BZj7DyyVfJSZ9IVJKLpIxegPdL4ej6\nfeRkTOLo+n2kjRvu+LrYVROn1ls1aTs1cXJdnJq3U2M7+Tv8x1mZLJgzs8U6AKz9cgvHTpxixbuL\nmDb5cWY8Nw+AMreH+a+/xds5c3k7Zy7zX3+LMrfHJ3k7+d97O2M77bzPaFCTOzL6EJPk4rmMJ/if\np1/lx7P+scXYP575j7z/1Ks8l/EEMUkubs/o7c3tnju5M7Mv//mjJ5k7dDJrc/7YYn9pO3wycOnU\npwulRwooO15IzYVq9i/fSNfM1EZtumXexZ6lawH4asVmbhnQHYCumansX76R6vNVlB0vpPRIAZ36\ndPFJ7DpdM1PZu3QdAHnbDxIcHkJIfGSjNiHxkQSFdiBv+0EA9i5dR7ehfWu3n1q//T1L19Yvd3Jd\n7KqJU+utmrSdmji5Lk7N26mxnfwd3rdPTyLCw7iUz9ZtZMQPB2NZFr17/H/27j2+qurO//9rJVwC\nud9PqBYioEUgEEGJI0oQwldDgSqdyswIsXWKlqLfnwUvlX6B4WIZpqIOKApSB+x4qeIotogCIwoo\n1yABdFq5KUgSEsnlJIRAyPr9cZKQkwRiGfbx7Ph++sjjkbPPWp/94bPXPrDca+/TC6+3gqLiE2ze\nupPrr00nOiqS6KhIrr82nc1bdwYkbzf/fe9kbLed91dnnatJrxED2PWG770ju/YTFtmZyET/2JGJ\nMXSM7MSRXfsB2PXGRq6uiz3on4azYfEqzp6uAaDy63LcojbIf4LVRU9cjDHvfNO2EZ5YvPknGl57\n808Q6Ylt1qb8mK+NPVtLtfcknWIjiGzat+AEEY36Ohm7XqQnlvJjX/u1i0z2bxeZHIu3oOU8OidE\nUXm8FIDK46V0TohyfV2cqomTsVUT1eSbxnZrXdyat1tju/kzvDWFRV/jSUpoeJ2clEBhUTGFRcV4\nkhLPbU/0bQ9E3m7++97J2G477yMSoxvaRSfHUnrsXL+yghNENdl/lCeW8kY5luWfILpu/wlXeEi9\n7iomvTmLn7/6/7gs7QqkbbvgPS7GmGvO9xbQ/wL9JgITAW6Pu44rufaiExQRERGRNuIS3pMeEhpK\np+gInvnRdC7r151/ePp+8P0bVXe+t1Gt3Zy/HfgA3yBoKqaFbQBYa5cASwDmd73TVhSUEJkS1/B+\nZEoc3oISvz4VBSVEdYmjouAEJjSEjpGdqSqpwNu0ryeOikZ9nYqdPmE4aeOGAlCQd5CoLvF81aid\nt9B/H97CEiI9Ledxsric8KQYKo+XEp4Uw8nickdzdyq2kzU5e7qGnNVzXVdv1aTt1cSNddHxbDvn\nT+N+TsX+JpIT4yk4fu5KSuHxYpITE0hOTGD7rrxz24uKuTY9LSB5u/Hve7eOQydrUnP6DPetfgyA\no7sPEtMlji/q+kV74ihvknt5QQlRjXKMTomjrG7/5QUn2Pfu9rpYB7C1FiABKCLIWc2tLkprS8U+\nA+6x1g5t+gMUt9K3Qf7ug8Smeoi+PJGQ9qH0GpXB/rW5fm32r8ulz9gbAbgq+zq+/OhT3/a1ufQa\nlUFoh3ZEX55IbKqH/E8OOB5714p1LM+exvLsaXz+3k56jx0MQEp6d6q9Jxsue9arPF7K6YoqUtJ9\na0N7jx3M/rU7m+2/z9gb+bxuu9vq4mRN9q7c6Mp6qyZtryZurIuOZ9s5f+o5GfubyBycwao167HW\nsnvvZ0REhJOYEMcNgwbw0bZcysq9lJV7+WhbLjcMOnc/hRtr4tYx7tbzPnflRhZmP8rC7Ef59L0d\npN/ue+/y9B6c8lbhLfKP7S0qpdpbxeXpvqfapd9+I5+954u9770dXJFxNQAJqR5C27eDv+Hfp+I+\nxtrzz/iMMT8G9lhr/9LCez+y1r7Z2g7md73Tgu+pFDdPvxMTGsKeP37AlkWrGPyrsRTkHWL/ulxC\nO7Zn5BP3kty7G6dKK1g1eRFlR3wT5ozJo+n7kyHYmlrWz3qRQxvy/PbhZOx6w2fnkDokjZqq07wz\ndQkFew4BkLN6LsuzpwHg6ZvKrY9PpF1YBw5t2M266b7H9YXFRDDmmfuI6hJP2VfFrJq0kFNllY7n\n7nRdnKqJW+utmrSdmri5Lm7N262x3fYZ/sDOWQA8OGMe23flUVpaTnxcDJPuHk9Nje8G5ztuG4m1\nlrkLnmHTlh10Cgtj9qMP0KfXlQC88ad3WbriVQAm5ozjtpEjAHhiwHRX1qQtjHE3nfcrfvkUVY1q\nMnrWXVw5pB9nqqp5/cHn+Kou9n2rH2Nh9qMAfK9vKj/+3b20D+vAXzfsZtWM/wAgtH0oY+ffQ8rV\nXTl7pobVc/+Tn7/8m5ZWCQWdf+7246C+5PL84deDso4XnLgAGGN+AHwP2GqtrWi0/RZr7ZrWdlA/\ncREREZFvV/3ExQn1ExeRCykxzj6z6reHXwrKf3A39bMgn7j8PkgnLhdcKmaMuR94C7gP2GuMGdPo\n7cecTExERERERKReazfn/xwYYK2tMMZ0A143xnSz1j5Fyzfsi4iIiIiIXHKtTVxC6peHWWsPG2My\n8U1euqKJi4iIiIjI30xPFbs4rT1VrNAY0/B9LXWTmB/ie9RcXycTExERERERqdfaxGUCUNB4g7W2\nxlo7AbjJsaxEREREREQaueBSMWvt0Qu8t/nSpyMiIiIiItJca/e4iIiIiIjIJeTsQ6HbrtaWiomI\niIiIiHzrdMVFRETkO8LJL4nUl1vKN+Hl7LedgriYJi4iIiIiIgFUa/U45IuhpWIiIiIiIhL0NHER\nEREREZGgp6ViIiIiIiIBpIViF0dXXEREREREJOhp4iIiIiIiIkFPS8VERERERAKoVovFLoquuIiI\niIiISNDTxEVERERERIKeloqJiIiIiASQ1VKxixKwiUvqkDSGzRiPCQ0h75UNbF38tt/7oR3aMXLB\nvST3TaWqxMuqyYsoP1oMwKBJo0i7IxN7tpZ1M1dw+MM9AYtdb9jM8VwxtD9nqqp5Z+oSCvcebtYm\nuU83sh+/h3ZhHTj4/iesn/kiAGHR4Yx+ejLRlyVSdrSItyYtpLr8pKvr4nTN3Vhvt+bt1thO1tvN\ndXFrbND501Zi/+axBXy4eRtxsTG8+Ydnmx1Day2/ffJZNn68nbCwjsydNoWrr+oBwFur1/Lc8lcA\nuCdnHGOyswKWdz2Nw8Ce9z+ecRe9h6ZzuqqaF6cu5ui+Q83ajJp6B9fdfhOdoyOY0junYXv363rx\n4+k5dPnB93nhvqf45J2tzfpK2xKQpWImxDB8dg6v5cxn2fCH6DU6g/ieXfza9L0jk1NllSwdMoUd\ny9aQ+cg4AOJ7dqHXqAx+n/Uwr+XMJ2vOXZgQE5DY9a4Y2o/YVA9Lh0zh3V8vI2vOXS3+OUfM/Slr\nHnmepUOmEJvqITUzDfCduF9s/pSlmVP5YvOnZEwa5eq6OF1zN9bbrXm7NbaT9XZzXdwa28nj6daa\nuDU2wI+ys3h2wZwWjx/Axo+38+XRY6x+dRkzH7qf2b9bBEBZuZfFL7zEy0uf5OWlT7L4hZcoK/cG\nLG/QOAz0WLk6sz+JqR7+JfP/8vKjSxk39+4W671nfS7/NmZas+0lx4p5ceoz7Hhrc4v9pO0JyMQl\npX93Sg8XUnakiNozZ/ns7S30yBrg16Zn1jXsXbkRgL+s3sb3b+gNQI+sAXz29hbOnq6h7EgRpYcL\nSenfPSCx6/XIGsC+lZsAyN91gLCocMKTYvzahCfF0CGiE/m7DgCwb+Umeo4YWLf/AQ3737tyY8N2\nt9bF6Zq7sd5uzdutsZ2st5vr4tbY9W10/rg/NsDA/n2JjorkfN7ftIXRtwzDGEO/Pr3weisoKj7B\n5q07uf7adKKjIomOiuT6a9PZvHVnwPKub6dxGLixkjbiWra98SEAh3d9TqfIcKIS/etd/155UWmz\n7SeOFnHsf77E2tpm70nbFJCJS4QnFm/+iYbX3vwTRHpim7UpP+ZrY8/WUu09SafYCCKb9i04QUSj\nvk7GrhfpiaX82Nd+7SKT/dtFJsfiLWg5j84JUVQe951wlcdL6ZwQ5eq6OF1zN9bbrXm7NTY4V283\n18WtsUHnT1uJ/U0UFn2NJymh4XVyUgKFRcUUFhXjSUo8tz3Rtz2QeWscBnasxCTHUtKo3qUFXxPj\nieO7oDbIf4LVBScuxpgoY8xvjTEvGmP+scl7z1yg30RjzA5jzI6tFZ9fqlxFREREROQ7qrWb818A\nPgdWAj8zxowF/tFaWw1knK+TtXYJsARgftc7bUVBCZEp52bQkSlxeAtK/PpUFJQQ1SWOioITmNAQ\nOkZ2pqqkAm/Tvp44Khr1dSp2+oThpI0bCkBB3kGiusTzVaN23kL/fXgLS4j0tJzHyeJywpNiqDxe\nSnhSDCeLyx3N3Y2x3Vpvt+bduJ+bYgei3m6si1tj6/xpe7G/ieTEeAqOn7uSUni8mOTEBJITE9i+\nK+/c9qJirk1PczxvjcPAxr5p/Aj+7h+GAfDF7gPEdolvaBPjiae00dUskaZaWyrW3Vr7iLX2TWvt\naCAX+G9jTHwr/fzk7z5IbKqH6MsTCWkfSq9RGexfm+vXZv+6XPqMvRGAq7Kv48uPPvVtX5tLr1EZ\nhHZoR/TlicSmesj/5IDjsXetWMfy7Gksz57G5+/tpPfYwQCkpHen2nuy4VJwvcrjpZyuqCIl3bd+\ns/fYwexfu7PZ/vuMvZHP67a7sS5OxXZrvd2at5PH0snYgai3G+vi1tg6f9pe7G8ic3AGq9asx1rL\n7r2fERERTmJCHDcMGsBH23IpK/dSVu7lo2253DDo3P0U+vu+bYyVD198j3nZDzMv+2Hy3tvOdbff\nBEC39J5UeU+2eC9LW1SLDeqfYGWsPX9yxpjPgN620V1Pxpi7gAeBCGtt19Z2ML/rnRZ8T+q4efqd\nmNAQ9vzxA7YsWsXgX42lIO8Q+9flEtqxPSOfuJfk3t04VVrBqsmLKDtSBEDG5NH0/ckQbE0t62e9\nyKENeX77cDJ2veGzc0gdkkZN1WnembqEgj2+x/XlrJ7L8mzfky48fVO59fGJtAvrwKENu1k3fQUA\nYTERjHnmPqK6xFP2VTGrJi3kVFml47m7NbZb6+3WvN0a28l6u7kubo3t5PF0a03cFvuBnbMAeHDG\nPLbvyqO0tJz4uBgm3T2empoaAO64bSTWWuYueIZNW3bQKSyM2Y8+QJ9eVwLwxp/eZemKVwGYmDOO\n20aOAOCJAdMDMgY1Dp2P/aU54xf/J7N+Rq8h/ThTdZo/PLiYL/ccBOCR1f/KvOyHARjzyD8xcMwN\nRCfHUlZYwsev/jern3yd76d15+fPTaFzdDg11WcoLyol5crLmz8uLgj9fdcxwTs7AF774q2grGNr\nE5f5wHvW2nVNtt8CLLTW9mxtB/UTFxEREWm76icuTqifuIj7NZ24XGqLDr8alP/gbkoTl4tzwXtc\nrLUPGWN+YIwZBmy11lbUbV9jjLk/IBmKiIiIiLQhNoiXYwWz1p4qdh/wFnAfsNcYM6bR23OdTExE\nRERERKRea08VmwgMsNZWGGO6Aa8bY7pZa58CgvISkoiIiIiItD2tTVxCGi0PO2yMycQ3eemKJi4i\nIiIiIn+zYP6Sx2DW2uOQC40x/etf1E1ifggkAH2dTExERERERKReaxOXCUBB4w3W2hpr7QTgJsey\nEhERERERaaS1p4odvcB7my99OiIiIiIibduFvo5Ezq+1Ky4iIiIiIiLfOk1cREREREQk6LX2VDER\nERGRVjn57fYP7JzlWGwn85bmutW2/7ZTCAq1+gLKi6IrLiIiIiIiEvQ0cRERERERkaCniYuIiIiI\niAQ93eMiIiIiIhJAtd92Ai6lKy4iIiIiIhL0NHEREREREZGgp6ViIiIiIiIBZPU45IuiKy4iIiIi\nIhL0NHEREREREZGgF9ClYsNmjueKof05U1XNO1OXULj3cLM2yX26kf34PbQL68DB9z9h/cwXAQiL\nDmf005OJviyRsqNFvDVpIdXlJwMSP3VIGsNmjMeEhpD3yga2Ln7bL2Zoh3aMXHAvyX1TqSrxsmry\nIsqPFgMwaNIo0u7IxJ6tZd3MFRz+cI9fXydjO11zxW4bsTW+W663m3NXbJ0/bTX2bx5bwIebtxEX\nG8Obf3i22TG01vLbJ59l48fbCQvryNxpU7j6qh4AvLV6Lc8tfwWAe3LGMSY7q1l/t9YF3HP+vD1p\nIdVlvtjdhqRx80xfTfa8soFtzzSvya1P+GpyqsTL27/01SQsJoLRz96Pp98V7HvtQ9ZPX9Esn2BX\nq6ViFyVgV1yuGNqP2FQPS4dM4d1fLyNrzl0tthsx96eseeR5lg6ZQmyqh9TMNMB30n6x+VOWZk7l\ni82fkjFpVEDimxDD8Nk5vJYzn2XDH6LX6Azie3bxi9n3jkxOlVWydMgUdixbQ+Yj4wCI79mFXqMy\n+H3Ww7yWM5+sOXdhQkxDPydjO11zxW4bsTW+W663m3NXbJ0/bTU2wI+ys3h2wZwWjx/Axo+38+XR\nY6x+dRkzH7qf2b9bBEBZuZfFL7zEy0uf5OWlT7L4hZcoK/f69XVzXdx0/gxqfP7MyWFlznxeGPYQ\nP7hATZbdNIUdz6/hpl/7anK2+gybH3+dD+a+1GIu0nYFbOLSI2sA+1ZuAiB/1wHCosIJT4rxaxOe\nFEOHiE7k7zoAwL6Vm+g5YiAAPbMGsHflRgD2rtzYsN3p+Cn9u1N6uJCyI0XUnjnLZ29voUfWAL+4\nPbOuaej7l9Xb+P4NvRty+uztLZw9XUPZkSJKDxeS0r97Qz8nYztdc8VuG7E1vluut5tzV2ydP201\nNsDA/n2JjorkfN7ftIXRtwzDGEO/Pr3weisoKj7B5q07uf7adKKjIomOiuT6a9PZvHWnX18318VN\n50+Puu2e/t0pOVxI2Ze+mvzP21voPsK/Jt1HXMO+1319/9qoJmeqqvlq+1+pOXUG+W4J2MQl0hNL\n+bGvG157C04QmRzr3yY5Fm/BiXNt8k8Q6fG16ZwQReXxUgAqj5fSOSEqIPEjPLF481vuUy/CE0v5\nMV8be7aWau9JOsVGENm0b8EJIhr1dTK2kzVR7LYTW+O75Xq7OXfF1vnTVmN/E4VFX+NJSmh4nZyU\nQGFRMYVFxXiSEs9tT/Rtb5qXW+vixvMn0hOL99i5PhX5LcRt1MaereV0XU3aAmttUP8EqwtOXIwx\nHmPMYmPM08aYeGPMTGPMHmPMH40xKRfoN9EYs8MYs2NrxeeXPmsREREREflOae3m/P8A/gyEA+8D\n/wlkAz8CngXGtNTJWrsEWAL8snDfFwMACvIOEtUlnq/q2kR64vAWlvj18xaWEOmJa3gdmRKHt8DX\n5mRxOeFJMVQeLyU8KYaTxeWkTxhO2rihOBUfoKKghMiUlvvUqygoIapLHBUFJzChIXSM7ExVSQXe\npn09cVQ06utEbCdrcvZ0DTmr5yp2G4it8X3+mrgxd7eOQ7fGduv54/bY30RyYjwFx89dSSk8Xkxy\nYgLJiQls35V3bntRMdempzXLy011cetnSv354y0oIbLLuT4RKS3ErWtTX5MOdTWR767WloolW2sX\nWmvnATHW2n+11h6x1i4Eun6D+E8vz57G8uxpfP7eTnqPHQxASnp3qr0nGy4d1qs8XsrpiipS0n1r\nN3uPHcz+tb41qPvX5dJn7I0A9Bl7I5+v3cmuFetwMj5A/u6DxKZ6iL48kZD2ofQalcH+tbl+cRv3\nvSr7Or786FPf9rW59BqVQWiHdkRfnkhsqof8Tw409HMitpM12btyo2K3kdga3+eviRtzd+s4dGts\nt54/bo/9TWQOzmDVmvVYa9m99zMiIsJJTIjjhkED+GhbLmXlXsrKvXy0LZcbBvnfT+G2urj1M6V+\ne0GTmvxgVAYHmtTkwNpcev/Y1/fK7Os4UleTtqA2yH+ClbnQOjZjzG5rbb+63+dYa3/T6L091tq+\nre1gftc7G3YwfHYOqUPSqKk6zTtTl1Cw5xAAOavnsjx7GgCevqnc+vhE2oV14NCG3ayre8RdWEwE\nY565j6gu8ZR9VcyqSQs5VVbpty+n4l8xtB83T7/T97i+P37AlkWrGPyrsRTkHWL/ulxCO7Zn5BP3\nkty7G6dKK1g1eRFlR4oAyJg8mr4/GYKtqWX9rBc5tCHPL2cnYztdc8VuG7E1vluut5tzV2ydP20t\n9gM7ZwHw4Ix5bN+VR2lpOfFxMUy6ezw1NTUA3HHbSKy1zF3wDJu27KBTWBizH32APr2uBOCNP73L\n0hWvAjAxZxy3jRwBwBMDpru2Lo255fz50y/OxU4d2o+hM+4kJDSEPa9+wNZFq7jhV2Mp2HOIA2t9\nNcl+8l6S6mryp8mLKPvSV5Ofb36CDpGdCG3fjuryk7x+5zy+/vwYU7/8g/8j14LU/7n81uC9kQR4\n98g7QVnH1iYus4D51tqKJtt7APOstT9ubQeNJy4iIiIif6v6iYsTGk9cxHkhDv+rUBOXSyNYJy4X\nXCpmrZ0OXGaMGWaMiWi0fT/wvNPJiYiIiIiIQOtPFbsPeAu4D9hrjGl8M/5jTiYmIiIiItIW2SD/\nL1i19lSxicAAa22FMaYb8Loxppu19ikgKC8hiYiIiIhI29PaxCWk/v4Wa+1hY0wmvslLVzRxERER\nERGRAGntcciFxpj+9S/qJjE/BBKAVp8oJiIiIiIi/mqxQf0TrFqbuEwAChpvsNbWWGsnADc5lpWI\niIiIiEgjF1wqZq09eoH3Nl/6dERERERERJpr7R4XERERERG5hC70PYpyfq0tFRMREREREfnW6YqL\niIiIBDUnv93+gZ2zHIvtZN5uVRpS+22nIC6miYuIiIiISAAF85O7gpmWiomIiIiISNDTxEVERERE\nRIKeloqJiIiIiASQ1VKxi6IrLiIiIiIiEvQ0cRERERERkaCniYuIiIiIiAQ93eMiIiIiIhJAtVb3\nuFwMXXEREREREZGgF9ArLsNmjueKof05U1XNO1OXULj3cLM2yX26kf34PbQL68DB9z9h/cwXAQiL\nDmf005OJviyRsqNFvDVpIdXlJwFIHZLGsBnjMaEh5L2yga2L3/aLGdqhHSMX3Ety31SqSrysmryI\n8qPFAAyaNIq0OzKxZ2tZN3MFhz/c0ywnJ+M7nbsba66aBDZv1aTlmqguyvvbzluxA3ssf/PYAj7c\nvI242Bje/MOzzfK11vLbJ59l48fbCQvryNxpU7j6qh4AvLV6Lc8tfwWAe3LGMSY7q1l/t45Dp/Me\nOWMCVw7tz5mq06yc+iz5+5rn3aVPKrf/7h7ah3Xgr+9/wp//ZQUAN/9/Yxk4biiVJ8oBWDv/j836\nStsSsCsuVwztR2yqh6VDpvDur5eRNeeuFtuNmPtT1jzyPEuHTCE21UNqZhrgG/xfbP6UpZlT+WLz\np2RMGgWACTEMn53DaznzWTb8IXqNziC+Zxe/mH3vyORUWSVLh0xhx7I1ZD4yDoD4nl3oNSqD32c9\nzGs588macxcmxPj1dTK+07m7seaqSWDzVk1aronqory/7bwVO/DH8kfZWTy7YE6LuQJs/Hg7Xx49\nxupXlzHzofuZ/btFAJSVe1n8wku8vPRJXl76JItfeImycq9fX7eOQ6fzvjKzP/GpHp7I/BVvPvo8\no+f+rMW8R8/5GW/++nmeyPwV8akeemb2a3hv87J3eDr7UZ7OfpS/bvikxf7ByAb5T7AK2MSlR9YA\n9q3cBED+rgOERYUTnhTj1yY8KYYOEZ3I33UAgH0rN9FzxEAAemYNYO/KjQDsXbmxYXtK/+6UHi6k\n7EgRtWfO8tnbW+iRNcAvbs+saxr6/mX1Nr5/Q++GnD57ewtnT9dQdqSI0sOFpPTv7tfXyfhO5+7G\nmqsmgc1bNWm5JqqL8v6281bswB/Lgf37Eh0Vyfm8v2kLo28ZhjGGfn164fVWUFR8gs1bd3L9telE\nR0USHRXJ9dems3nrTr++bh2HTufda8QAPnnD1/forv2ERXYmItE/74jEGDpGduLorv0AfPLGRq5u\n8nkt3x1/88TFGJN0MTuK9MRSfuzrhtfeghNEJsf6t0mOxVtw4lyb/BNEenxtOidEUXm8FIDK46V0\nTogCIMITize/5T71IjyxlB/ztbFna6n2nqRTbASRTfsWnCCihb5OxXc6dzfWXDUJbN6qScs1UV2U\n97edt2IH/li2prDoazxJCQ2vk5MSKCwqprCoGE9S4rntib7tTfNy4zh0PO/kWMqOnWtTXnCCqCZt\nojyxlDeKU5bv/2fLyBnB5Hfmcdv8iYRFhSNt2wXvcTHGxDXdBGwzxqQDxlp7ooVuGGMmAhMBbo+7\njkERPS9FriIiIiIiAGz9w1re//c3wMKwKX/Prb/5p287pW+sNqgXZP3v1c0hXgW6AYeBn1hrS5q0\n6Qr8F74LKe2Bhdba5jeYNdLazfnFwBdNtn0PyMW3BO6KljpZa5cAS4BfFu77YgBAQd5BorrE81Vd\nm0hPHN5Cv/zxFpYQ6Tk3V4pMicNb4Gtzsric8KQYKo+XEp4Uw8li341YFQUlRKa03KdeRUEJUV3i\nqCg4gQkNoWNkZ6pKKvA27euJo6KFvk7FdyJ2+oThpI0biltrrpoEJu+zp2vIWT1XNWmSt8aK8v62\n83by3HRr7ED9fd+a5MR4Co6fu5JSeLyY5MQEkhMT2L4r79z2omKuTU9rlpebxqGTNYvEdgIAACAA\nSURBVB80PouB/+DL+6vdB4nucq5NlCeO8ibxywtKiGoUJzrl3J+tsi5PgB2v/Dfjlz2IBI1HgPXW\n2nnGmEfqXj/cpE0+cL21ttoYEwHsNcasstYeO1/Q1paKPQj8BRhtrU211qYCR+t+b3HS0sTTy7On\nsTx7Gp+/t5PeYwcDkJLenWrvyYbLkvUqj5dyuqKKlHTfGsjeYwezf61vnej+dbn0GXsjAH3G3sjn\nddvzdx8kNtVD9OWJhLQPpdeoDPavzfWL27jvVdnX8eVHn/q2r82l16gMQju0I/ryRGJTPeR/csCv\nr5PxnYi9a8U63Fxz1SQwee9duVE1aSFvjRXl/W3n7eS56dbYgfr7vjWZgzNYtWY91lp27/2MiIhw\nEhPiuGHQAD7alktZuZeyci8fbcvlhkH+94G4bRw6WfOtL65tuJn+0/d20P92X9/L0ntQ7a2iosg/\n74qiUqq9VVyW7nuCW//bb+Sz93z5Nb4f5ur/cy2Ffz3a2mGUwBkDLK/7fTnwo6YNrLWnrbXVdS87\n8g1uYTG2lS/AMcZcBjwBHAFmALu/4aQFgPld72zYwfDZOaQOSaOm6jTvTF1CwZ5DAOSsnsvy7GkA\nePqmcuvjE2kX1oFDG3azbrrvkXdhMRGMeeY+orrEU/ZVMasmLeRUWSXge5rGzdPvxISGsOePH7Bl\n0SoG/2osBXmH2L8ul9CO7Rn5xL0k9+7GqdIKVk1eRNmRIgAyJo+m70+GYGtqWT/rRQ5tyKMpJ+M7\nnbsba66aBDZv1aTlmqguyvvbzluxA3MsH9g5C4AHZ8xj+648SkvLiY+LYdLd46mpqQHgjttGYq1l\n7oJn2LRlB53Cwpj96AP06XUlAG/86V2WrngVgIk547ht5AgAnhgwvaEmbh2HlzrvclPrl/cPZ93F\nlUP6cbqqmjcefI5jdXn/cvVjPJ39KABd+qYy9nf3+h6HvGE3f5rxHwD8eMEv8FzdFSyUHC3irUeX\n8cj2Z/wfXRakrv/e0KBeK/bxV+//r+pojCm11sbU/W6AkvrXTdpdDvwZ6AE8aK19+oJxW5u4NAo8\nGngU6Gat9XzTxBtPXERERESCSf3ExQmNJy7i03TicqnNOfySJi6XwJZjG+6h7n71OkvqbgVpYIxZ\nB7Q0J5gGLG88UTHGlFhrz/tEDGNMF+BNYJS1tvB87Vr9AkpjzA/w3dfy38BaoHvd9lustWta6y8i\nIiIiIu7R6H71C7UZfr73jDGFxpgUa22+MSYFON5KrGPGmL3AjcDr52t3wbVkxpj7gbeA+4C9wAhr\n7d66tx+7UF8REREREWnOWhvUP5fAKiCn7vccfPMJP8aYy4wxnep+jwUG47u3/rxau+Lyc2CAtbbC\nGNMNeN0Y081a+xS+RyOLiIiIiIg0Ng/4ozHmbnxPKP4JgDFmIHCvtfafgV7A48YYi29e8Ttr7Z4L\nBW1t4hJira0AsNYeNsZk4pu8dEUTFxERERERacJa+zUwrIXtO4B/rvt9LZDWtM2FtPbYsUJjTP9G\nO6sAfggkAH3/lh2JiIiIiIjvCyiD+SdYtTZxmQAUNN5gra2x1k4AbnIsKxERERERkUYuuFTMWnve\nb/Kx1m6+9OmIiIiIiIg01+o3VIqIiIiIiHzbWv0eFxERERERuXRsEN9HEsw0cREREZHvLCe/3f6B\nnbMci+1k3k6KslrsIxdPo0dERERERIKerriIiIiIiATQJfp2+u8cXXEREREREZGgp4mLiIiIiIgE\nPS0VExEREREJoGD+dvpgpisuIiIiIiIS9DRxERERERGRoKelYiIiIiIiAaSnil0cXXEREREREZGg\nF9ArLsNmjueKof05U1XNO1OXULj3cLM2yX26kf34PbQL68DB9z9h/cwXAQiLDmf005OJviyRsqNF\nvDVpIdXlJwFIHZLGsBnjMaEh5L2yga2L3/aLGdqhHSMX3Ety31SqSrysmryI8qPFAAyaNIq0OzKx\nZ2tZN3MFhz/cE9Dc3VwX1aRt1ET1brkmqktg83ZrbCfr7dbYbj6WTsX/zWML+HDzNuJiY3jzD882\n26+1lt8++SwbP95OWFhH5k6bwtVX9QDgrdVreW75KwDckzOOMdlZzfqDO8eKE7GlbQvYFZcrhvYj\nNtXD0iFTePfXy8iac1eL7UbM/SlrHnmepUOmEJvqITUzDfB9IHyx+VOWZk7li82fkjFpFAAmxDB8\ndg6v5cxn2fCH6DU6g/ieXfxi9r0jk1NllSwdMoUdy9aQ+cg4AOJ7dqHXqAx+n/Uwr+XMJ2vOXZgQ\nE7Dc3VwX1aRt1ET1brkmqktg83ZrbCfr7dbYbj6WTsb/UXYWzy6Y02KNATZ+vJ0vjx5j9avLmPnQ\n/cz+3SIAysq9LH7hJV5e+iQvL32SxS+8RFm5t1l/N44Vp2MHu1psUP8Eq4BNXHpkDWDfyk0A5O86\nQFhUOOFJMX5twpNi6BDRifxdBwDYt3ITPUcMBKBn1gD2rtwIwN6VGxu2p/TvTunhQsqOFFF75iyf\nvb2FHlkD/OL2zLqmoe9fVm/j+zf0bsjps7e3cPZ0DWVHiig9XEhK/+4By93NdVFN2kZNVO+Wa6K6\nBDZvt8Z2st5uje3mY+lk/IH9+xIdFcn5vL9pC6NvGYYxhn59euH1VlBUfILNW3dy/bXpREdFEh0V\nyfXXprN5685m/d04VpyOLW1TwCYukZ5Yyo993fDaW3CCyORY/zbJsXgLTpxrk3+CSI+vTeeEKCqP\nlwJQebyUzglRAER4YvHmt9ynXoQnlvJjvjb2bC3V3pN0io0gsmnfghNENOnrZO5urotq0jZq4mRs\nN9dbdQls3m6NDe48N52M7eZjGYh/T5xPYdHXeJISGl4nJyVQWFRMYVExnqTEc9sTfdubcuNYcTq2\ntE0XnLgYY25p9Hu0MWaZMSbPGPOSMSb5Av0mGmN2GGN2bK34/FLmKyIiIiIi30Gt3Zz/GLCm7vfH\ngXxgFHA78Bzwo5Y6WWuXAEuAXxbu+2IAQEHeQaK6xPNVXZtITxzewhK/ft7CEiI9cQ2vI1Pi8Bb4\n2pwsLic8KYbK46WEJ8VwsrgcgIqCEiJTWu5Tr6KghKgucVQUnMCEhtAxsjNVJRV4m/b1xFFR1zd9\nwnDSxg3FidzPnq4hZ/VcR2I7WRfVpO3URPVuOW+n47u1Lk7l3bifm2LrvG974yQQ8S8kOTGeguPn\nrqQUHi8mOTGB5MQEtu/KO7e9qJhr0333d2gctjwOw6LDz1/oIGKD+D6SYPa3LBUbaK39jbX2C2vt\nE0C3b9Dn6eXZ01iePY3P39tJ77GDAUhJ706192TD5b16lcdLOV1RRUq6b11o77GD2b/Wt5Zz/7pc\n+oy9EYA+Y2/k87rt+bsPEpvqIfryRELah9JrVAb71+b6xW3c96rs6/jyo09929fm0mtUBqEd2hF9\neSKxqR7yP/Gtody1Yh1O5b535UbHYjtZF9Wk7dRE9W45b6fju7UuTuVdz22xdd63vXESiPgXkjk4\ng1Vr1mOtZffez4iICCcxIY4bBg3go225lJV7KSv38tG2XG4Y5LvvRuPw/ONQ2i5zoS/AMcYcBRYA\nBvgl0N3WdTDG5Flr01rbwfyudzbsYPjsHFKHpFFTdZp3pi6hYM8hAHJWz2V59jQAPH1TufXxibQL\n68ChDbtZN30FAGExEYx55j6iusRT9lUxqyYt5FRZJeB7KsXN0+/EhIaw548fsGXRKgb/aiwFeYfY\nvy6X0I7tGfnEvST37sap0gpWTV5E2ZEiADImj6bvT4Zga2pZP+tFDm3IoyVO5e7muqgmbaMmqnfL\nNVFdApu3W2M7PQ7dGNvNx/JSx39g5ywAHpwxj+278igtLSc+LoZJd4+npqYGgDtuG4m1lrkLnmHT\nlh10Cgtj9qMP0KfXlQC88ad3WbriVQAm5ozjtpEjAHhiwHSNwxZi35/3XPPHwwahNM/1QX3JJa/g\n46CsY2sTlxlNNj1jrS0yxniA+dbaCa3toPHERUREROS7on7i4oSmExfxeeiLPwTlP7ib6pOcEdT/\nPt5buCUo63jBe1ystf9ijPkB8D1gq7W2om57gTHmpUAkKCIiIiIi0tpTxe4D3gLuA/YaY8Y0evsx\nJxMTERERERGp19pTxSYCA6y1FcaYbsDrxphu1tqn8N33IiIiIiIifwM9VezitDZxCWm0POywMSYT\n3+SlK5q4iIiIiIhIgLT2OORCY0z/+hd1k5gfAglAXycTExERERERqdfaFZcJQE3jDdbaGmCCMeY5\nx7ISEREREWmjai/wVF85v9aeKnb0Au9tvvTpiIiIiIiINNfaUjEREREREZFvXWtLxURERERE5BLS\nU8UujiYuIiIiIg5w8tvtH9g5y7HYTubdqdax0PIdoKViIiIiIiIS9HTFRUREREQkgPRUsYujKy4i\nIiIiIhL0NHEREREREZGgp4mLiIiIiIgEPd3jIiIiIiISQHoc8sXRFRcREREREQl6mriIiIiIiEjQ\n01IxEREREZEA0uOQL07AJi6pQ9IYNmM8JjSEvFc2sHXx237vh3Zox8gF95LcN5WqEi+rJi+i/Ggx\nAIMmjSLtjkzs2VrWzVzB4Q/3tLiPYTPHc8XQ/pypquadqUso3Hu4WZvkPt3Ifvwe2oV14OD7n7B+\n5osAhEWHM/rpyURflkjZ0SLemrSQ6vKTAcldefvnrZoEtiaK3XJst44VJ/N2a2wn6+3W2DqWLdfb\n6fiXMjYmBGwtv3lsAR9u3kZcbAxv/uHZZvGstfz2yWfZ+PF2wsI6MnfaFK6+qgcAb61ey3PLXwHg\nnpxxjMnOatbfyeP5/cw0bprpi/3pyxvY+Yx/7JAO7Rjx5L0k9k3lVImXNZMW4T1aTHL/Kxg6724A\njIGtT/wXB9fsaJa7tD0BWSpmQgzDZ+fwWs58lg1/iF6jM4jv2cWvTd87MjlVVsnSIVPYsWwNmY+M\nAyC+Zxd6jcrg91kP81rOfLLm3IUJMc32ccXQfsSmelg6ZArv/noZWXPuajGXEXN/yppHnmfpkCnE\npnpIzUwDfCfXF5s/ZWnmVL7Y/CkZk0YFJHfl7Z+3ahLYmih2y7HdOlaczNutsZ2st1tj61i2XG+n\n41/q2CGdYgD4UXYWzy6Y02IsgI0fb+fLo8dY/eoyZj50P7N/twiAsnIvi194iZeXPsnLS59k8Qsv\nUVbu9evr9FjJnJPDqgnz+c+bH+LKMRnENonde1wmp0orefHGKXzy/BpueNQX++v/OcqrI/8fr9wy\njbfG/xtDf/tTTKjufvguCMhRTunfndLDhZQdKaL2zFk+e3sLPbIG+LXpmXUNe1duBOAvq7fx/Rt6\nA9AjawCfvb2Fs6drKDtSROnhQlL6d2+2jx5ZA9i3chMA+bsOEBYVTnhSjF+b8KQYOkR0In/XAQD2\nrdxEzxED6/Y/oGH/e1dubNjudO7K2z9v1SSwNVHslmO7daw4mbdbYztZb7fG1rFsud5Ox7/UsUM6\ndAZgYP++REdFcj7vb9rC6FuGYYyhX59eeL0VFBWfYPPWnVx/bTrRUZFER0Vy/bXpbN6606+vk8cz\nuS52+Ze+2H9dtYUrRvjHTh1xDf/zui/2/j9v47K62DWnTmPP1gLQrmN73PiALhvk/wWrv3niYoyJ\n/1v7RHhi8eafaHjtzT9BpCe2WZvyY7429mwt1d6TdIqNILJp34ITRDTpCxDpiaX82Nd+7SKT/dtF\nJsfiLWg5j84JUVQeLwWg8ngpnROiApK78vbPWzUJbE0Uu+XYbh0rTubt1tjg3nGocRLYY+l0/Esd\nm5BQvonCoq/xJCU0vE5OSqCwqJjComI8SYnntif6tjfm5PEM98RScezc+xX5zY93hCcWb6PYp70n\nCYuN8OXbvzv/uG4e/7D2t7z/6AsNExlp2y44cTHGzDPGJNT9PtAYcxDYaoz5whgzJCAZioiIiIg0\nUvjJAV4a/gh//OF0Bv5yFKEd23/bKUkAtHZz/khr7SN1v/8bcIe1drsx5krgJWBgS52MMROBiQC3\nx11Hl4IeRKbENbwfmRKHt6DEr09FQQlRXeKoKDiBCQ2hY2Rnqkoq8BaU+Pf1xFFR1zd9wnDSxg0F\noCDvIFFd4vmqUTtvof8+vIUlRHpazuNkcTnhSTFUHi8lPCmGk8XlDXld6tyVd/O8z56uIWf1XNUk\nQDVR7POPE3DfWHFyjDtZE7fW2+1jXMfSvyZuHSvUnuWbSE6Mp+D4uSsphceLSU5MIDkxge278s5t\nLyrm2vQ0v75OjpXKghIiupx7PyLF//2G/XeJo7IudofIzpwqqfBrU7L/GKcrTxF/1WUczzv0jWoS\nDKzVFaKL0dpSsXbGmPrJTSdr7XYAa+1fgY7n62StXWKtHWitHTgooif5uw8Sm+oh+vJEQtqH0mtU\nBvvX5vr12b8ulz5jbwTgquzr+PKjT33b1+bSa1QGoR3aEX15IrGpHvI/8a393LViHcuzp7E8exqf\nv7eT3mMHA5CS3p1q78mGS6r1Ko+XcrqiipR03xrL3mMHs3/tzmb77zP2Rj6v2+5E7sq7ed57V25U\nTQJYE8U+/zgB940VJ8e4kzVxa73dPsZ1LP1r4taxUnva/2lo55M5OINVa9ZjrWX33s+IiAgnMSGO\nGwYN4KNtuZSVeykr9/LRtlxuGOR/j4mTY6Vw90FiunmIqot95egMDjWJfWhtLj/4sS92j5HXcXSz\nL3bU5YkNN+NHfi+e2B5dKD9S9I3qIe5m7AWeI22MuQ8YBcwDbgJigTeAm4ErrLXjW9vB/K53WvA9\nTePm6XdiQkPY88cP2LJoFYN/NZaCvEPsX5dLaMf2jHziXpJ7d+NUaQWrJi+irG4QZkweTd+fDMHW\n1LJ+1osc2pDX4r6Gz84hdUgaNVWneWfqEgr2+GbeOavnsjx7GgCevqnc+vhE2oV14NCG3aybvgKA\nsJgIxjxzH1Fd4in7qphVkxZyqqySQOSuvP3zVk0CWxPFbjm2W8eKk3m7Nbabx6HGSWCPpZuO5/d6\nRYCt5cEZ89i+K4/S0nLi42KYdPd4ampqALjjtpFYa5m74Bk2bdlBp7AwZj/6AH16XQnAG396l6Ur\nXgVgYs44bhs5AoAnBkx37Hh2anShoevQftw4805CQkP49NUP2LFwFYOmjOV43iEOrfXFznryXhL7\ndKO6tII1v1xE+ZdFXHX7DQyYNIramrPYWsv2p/6Lg+/6Jnj3HflD80fPBqHU+H7Bewc8cOjr3UFZ\nxwtOXACMMZnAL4Ar8S0tOwK8CbxgrT3T2g7qJy4iIiIicmk8sHOWY7EbT1wutU4Or5Byy8Sla3xa\nUP/7+Iuv84Kyjt/kCygLgCXAVmttw8JCY8wtwBqnEhMREREREanX2lPF7gfeAu4D9hpjxjR6+zEn\nExMREREREanX2hWXnwMDrLUVxphuwOvGmG7W2qeAoLyEJCIiIiIibU9rE5eQ+uVh1trDdfe7vG6M\n6YomLiIiIiIif7PW7jGXlrX2OORCY0z/+hd1k5gfAglAXycTExERERERqdfaxGUCvpvzG1hra6y1\nE/A9HllERERERMRxF1wqZq09eoH3Nl/6dERERERE2rZatFTsYrR2xUVERERERORbp4mLiIiIiIgE\nvW/yBZQiIiIiEkSc/Hb7B3bOciz2v1/jXN5uoqeKXRxdcRERERERkaCniYuIiIiIiAQ9LRUTERER\nEQmgWi0Vuyi64iIiIiIiIkFPExcREREREQl6WiomIiIiIhJAVl9AeVF0xUVERERERIKeJi4iIiIi\nIhL0NHEREREREZGgF7B7XFKHpDFsxnhMaAh5r2xg6+K3/d4P7dCOkQvuJblvKlUlXlZNXkT50WIA\nBk0aRdodmdiztaybuYLDH+5pcR/DZo7niqH9OVNVzTtTl1C493CzNsl9upH9+D20C+vAwfc/Yf3M\nFwEIiw5n9NOTib4skbKjRbw1aSHV5ScDkrtTebu1Jm6tt2rStmqimivvtvw5q7xbPpZuzd2pvH/z\n2AI+3LyNuNgY3vzDs81ytdby2yefZePH2wkL68jcaVO4+qoeALy1ei3PLX8FgHtyxjEmO6tZ/25D\n0hg605f33lc2sO2Z5nnf+sS9JPVN5VSJlz/90pd3WEwEo569H0+/K9j32of89/QVzWIHO6vHIV+U\ngFxxMSGG4bNzeC1nPsuGP0Sv0RnE9+zi16bvHZmcKqtk6ZAp7Fi2hsxHxgEQ37MLvUZl8Push3kt\nZz5Zc+7ChJhm+7hiaD9iUz0sHTKFd3+9jKw5d7WYy4i5P2XNI8+zdMgUYlM9pGamAb4T94vNn7I0\ncypfbP6UjEmjApK7U3m7tSZurbdq0rZqopor77b8Oau8Wz6Wbs3dybx/lJ3FswvmtJgnwMaPt/Pl\n0WOsfnUZMx+6n9m/WwRAWbmXxS+8xMtLn+TlpU+y+IWXKCv3+vU1IYZhc3J4I2c+/zHsIa4anUFc\nk7z71OX9+5umsPP5Ndz0a1/eNdVn+Ojx1/lg7kvnzU3apoBMXFL6d6f0cCFlR4qoPXOWz97eQo+s\nAX5temZdw96VGwH4y+ptfP+G3gD0yBrAZ29v4ezpGsqOFFF6uJCU/t2b7aNH1gD2rdwEQP6uA4RF\nhROeFOPXJjwphg4RncjfdQCAfSs30XPEwLr9D2jY/96VGxu2O527U3m7tSZurbdq0rZqopor77b8\nOau8Wz6Wbs3dybwH9u9LdFQk5/P+pi2MvmUYxhj69emF11tBUfEJNm/dyfXXphMdFUl0VCTXX5vO\n5q07/fp66vP+0pf3X97eQo8R/nn3GHEN+1735f3XRnnXVFXz1fa/cvbUmfPmJm1TQCYuEZ5YvPkn\nGl57808Q6Ylt1qb8mK+NPVtLtfcknWIjiGzat+AEEU36AkR6Yik/9rVfu8hk/3aRybF4C1rOo3NC\nFJXHSwGoPF5K54SogOTuVN5urYlb662atK2aqObKuy1/zirvlo+lW3MPxL+xzqew6Gs8SQkNr5OT\nEigsKqawqBhPUuK57Ym+7U1z8h7zzzsiuYXP4hbybgtqsUH9E6wuOHExxuQaY35jjGl+iUNERERE\nRCRAWrs5PxaIAd43xhQALwOvWmuPXaiTMWYiMBHg9rjr6FLQg8iUuIb3I1Pi8BaU+PWpKCghqksc\nFQUnMKEhdIzsTFVJBd6CEv++njgq6vqmTxhO2rihABTkHSSqSzxfNWrnLfTfh7ewhEhPy3mcLC4n\nPCmGyuOlhCfFcLK4vCGvS527k3mfPV1Dzuq5rqtJ435uqrdq0nZq0rSvaq6829rnrI7l+fN2c+5O\nfx5eSHJiPAXHz11JKTxeTHJiAsmJCWzflXdue1Ex16anNcspsot/3hWFLXwWt5C3fHe1tlSsxFo7\n1Vr7fWAK0BPINca8Xzc5aZG1dom1dqC1duCgiJ7k7z5IbKqH6MsTCWkfSq9RGexfm+vXZ/+6XPqM\nvRGAq7Kv48uPPvVtX5tLr1EZhHZoR/TlicSmesj/xLf2c9eKdSzPnsby7Gl8/t5Oeo8dDEBKeneq\nvScbLqnWqzxeyumKKlLSfReQeo8dzP61O5vtv8/YG/m8brsTuTuZ996VG11Zk3puq7dq0nZq0phq\nrrzb4uesjuX583Zz7k5/Hl5I5uAMVq1Zj7WW3Xs/IyIinMSEOG4YNICPtuVSVu6lrNzLR9tyuWGQ\n//0rBbsPEpPqIaou76tGZXCgSd4H1ubS+8e+vK9slHdbYK0N6p9gZS6UnDEm11p7TZNtoUAWcIe1\n9qet7WB+1zst+J6mcfP0OzGhIez54wdsWbSKwb8aS0HeIfavyyW0Y3tGPnEvyb27caq0glWTF1F2\npAiAjMmj6fuTIdiaWtbPepFDG/Ja3Nfw2TmkDkmjpuo070xdQsGeQwDkrJ7L8uxpAHj6pnLr4xNp\nF9aBQxt2s67uEXphMRGMeeY+orrEU/ZVMasmLeRUWSWByN2pvN1aE7fWWzVpWzVRzZV3W/6cVd4t\nH0u35n6p835g5ywAHpwxj+278igtLSc+LoZJd4+npqYGgDtuG4m1lrkLnmHTlh10Cgtj9qMP0KfX\nlQC88ad3WbriVQAm5ozjtpEjAPj3a6Y31CN1aD8yZ9xJSGgIe1/9gK2LVvF3vxpL4Z5DHFjry/vW\nJ+8lqS7vP09eRNmXvrz/efMTdIjsRGj7dlSXn+T1O+dx4vNjTPnyD80fPRuEEqKuDN7ZAVBc/teg\nrGNrE5dXrLXj/jc7qJ+4iIiIiEjwq5+4OKHxxMUJmrhcGsE6cbngPS7W2nHGmB8A3wO2WmsbFhYa\nY26x1q5xOkERERERkbakNoiXYwWz1p4qdh/wFnAfsNcYM6bR2485mZiIiIiIiEi91p4qNhEYYK2t\nMMZ0A143xnSz1j4FBOUlJBERERERaXtam7iE1C8Ps9YeNsZk4pu8dEUTFxERERGRv1kwP7krmLX2\nOORCY0z/+hd1k5gfAglAXycTExERERERqdfaxGUCUNB4g7W2xlo7AbjJsaxEREREREQaae2pYkcv\n8N7mS5+OiIiIiEjbVouWil2M1q64iIiIiIiIfOs0cRERERERkaDX2lPF/tc+tCWOxV5T8IljsUd5\nrnEs9k9qohyLPf3M/zgWe3m7bo7F7nF1sWOx5xzwOBY7xuFTqJQax2JfZts7FrvWscjQzsGr62EO\nxj7p0v9NFOpgTTo6GDs/1LlRODHuuGOxAbbkJzsW+xMHi+7lrGOxu9U693lVGuLcWImyzp34nRz8\noHXy2+3vz53lWGxp+xyfuIiIiMg34+SkRUSChx6HfHFc+v8ARURERETku0QTFxERERERCXpaKiYi\nIiIiEkC1Wip2UXTFRUREREREgp4mLiIiIiIiEvS0VExEREREJIAsWip2MXTFRUREREREgp4mLiIi\nIiIiEvS0VExEREREJID0VLGLE9CJy8R/uYeBQwdSXVXNk1Oe4MDeA37vdwzrvToGfAAAIABJREFU\nyCOLf42nq4fa2lq2rdvG8nn/AcCtd97KyAk/pPZsLVUnq1j0yEIo+KSh7xMLZnHrLTdzsqqKu+9+\ngF2f7G22//VrX8OTkkxV1SlfzOx/oKjoayb+fDy/+EUOZ8/WUllRyb2THoKSc/3u/peJDBg6gOqq\nahZOeYqDTfLuENaRBxc/jKdrCrW1texYt40X5y0H4OrrevOzGT+nW69uPD55Ph+v/sivb0pmGtfM\nHo8JCeHAyxv4bNHbfu8nDvoB18y6k5he3+ejXyziyJ+3NbyX+vc30vv//giAfU+9yaHXNjb7M/+/\nxx5kyPAbqDp5iofvn8mnef/TrM2yVxeSmJRAu3ah7Niyi5kP/yu1tbX8oHdPZv3bo3QO78xXR44R\nOnkJZyuqAIgZ2p/UWT+D0BCOv7Serxb9l/+f655RJP/jMGxNLWe+LuPAr56h+mgRnXt3o/u8iYRG\ndsaereXoU6/z9Sr/mnS49joifnkfhIRwavWfOfnKS81yBuh4401Ez5zNiV9MpOavfwGg8z/8E2G3\nZkNtLRWL/p3TO7Y36zd2xl1cPTSd01XV/OfUxRzdd6hZm5FT7+C622+ic3QED/bOadjerkM77lzw\nSy7vcwWVpV7emLyI0qPFDe9nz5hAz6H9OFN1mv+a+hz5+w43i53Spxu3/+5e2oW15/P3d7P6X1b4\nvf93/5zNLb/5J+al30NpSakjef/H5KfgiC926pA0hs0YjwkNIe+VDWxd7D8GQzu0Y+SCe0num0pV\niZdVkxdRXvdnHjRpFGl3ZGLP1rJu5goOf7inWU7DZ46n+9D+nKmq5s9Tl1C4t3lNkvt0Y+Tj99A+\nrAMH3v+EdTNfBOCq7OsY/MDtJPTowvLRMyjYc+7P3G1IGjfP9OW955X/v73zjo+ieP/4e1KA9Aq5\nBBBCFaQ3kZpQogQBBQuKFLEBShEUKV9QaSIqoKCIgF0UBRSUroAoSu+9CyGFhJAeUuf3x26Su8sl\nwWRPg79587oXd7s7n3vuufnM3OzOTraz54PCcfeYp8V980YyPz6vxV3J253eH47C1LQWx7/bwS9T\nLfOfxx0hTeio65/4ejsHrPQdKjjRff4wKuv6m0YsJDkijirNahE6+ykAhIA9877nwsZ9hfS7vjaQ\nWnpeNhSTl/B3nsOpUgUubDvEL3peKnm50fv9F/CqVpnEiFjWjFhARlKaXbR/GrGAjMS0/JyH6jk5\nVkzOq+g5+cks573Mcr7VRs5Lm+883IP8eHzrm+ydt5qDi9cX0u/56iDqhzYjKz2TVS99SKQNbwY1\nCqbf21o9PL3tEOvMvNl2cBhtB4WRm5NLpT93kvDuEi1f7Vrj+9IIcHQg5fsNJH36jYWme7/78Xik\nD+TmkJt2k/gZc8m6eLkgZ6YqBK1cRuLiz0n64juLsqbQJjSfpuXkwvLtnLLuH9reSfNpT+DV4A7+\nHLaQCLP+odPy8fi1qEPcnjP8NujtQp8VoJeek8z0TFYWk5OHzXLyo1lO7tFzInNyObX1IN/N/jJ/\n30OvDuEuvb36ooj2qpdZezXOrL2q3aYBD00dTNCdd/DJyHc5tGF3/j57+x60ulLPrK7YaseDGgXT\nV8/LGbO60mVMP1r1DyU1PgmAnXO+48K2w/nljPTm5uEF3rwjpAmdzPyz34Z/wsz8s1H3T4BVe7Xb\nRntlL9//b9Zcduzcg6+PNz98+WGhPEgpeWP+h/z2514qVarIzMnjaFi/DgBr1m9h8Wea154b3J8+\n4d0LlVf8N/nHpoq1Cm1FUM0gnu30DAsnLGDEzOdtHrf6o9UM7zKM0T1G0bBVA1qGtARg+w/beSHs\neUb1GMmqD1fx9JRn8sv0uK8LdesEc2fDDgwf/grvL3yjyDgGDXqBVq3DaNU6jNjY6wB8/c33NG/R\njVatw3jrnQ94e86r+ce3CG1JUM0gRnR6jkUT3ue5mcNt6q756HtGdhnOuB6jubNVA1roccdGxrJg\n3Hx2rPm1UBnhIGg5awjbB8xhfch4avS5B8+6VS2OSbsax+4xi/nre6sf995uNBrbl833T2VTzyk0\nGtsXZy9Xi2M6d2tPjVrV6dbmAaaMm8G0ORNtxj76qQn0Dn2M8I6P4OvvQ4/e3QCYOW8Kb89YwP2d\nH2XL+m0EjeijFXBwoNasZzgxYCaHOo/B/4EOuNSrZqGZevQiR+4bz+GuY7n+0y5q/G8gALnpGZwd\ntYBDIWM48fh0gqcNxdHTLG4HBzxGjSFh4njihw6mYpeuONaoUTh3Li649H2IrBPH87c51qhBxdAu\nxD81hIQJL+Mx+kVwsKziDUOaUTnYxPSQ0ayYtIRHZj5lMyfHfznAO30mF9re9pEupCWmMj1kNNuX\nraf7hMfy99UNaYpfsIl3Q8axdtIyes180qZ2rxlDWTNxKe+GjMMv2ETdkKb5+zwDfanTqbHFYMge\ncfee8Dig1cFu0wfz3eA5LOs2nga92+JXN8iibONHQ7iZmMqSzuPYt2wjIRP6A+BXN4gGvdrycfdX\n+G7wHLrPGIJwEBZla4U2xSfYxOLO49g4cRn3zhhiM+57Zz7JxglLWdx5HD7BJmqFNAEg7kwE3z/3\nLld2n7Y4XjgIus0YzKrBc/ik63juLCbuZZ3GsW/pRjpN1OLOychi5zsr+XWm7QFxnn7nGYP5cdAc\nlncZT70+bfGx0m/YP4SMhFS+7DiOw0s30m6Sph9/KoJve05hxX2TWTvwLULeeBLhaFkP8/KypPM4\nNk1cRvci8hKm52WJnpdgPS93j+jFXztPsCTkJf7aeYK2I3rZTbuNri0cBF1nDGb14Dl82nU89Xu3\nxdcqJ430nH/caRz7zXKenZHFH8XkvCz5zqPD1AFcNvuBaE69kGb4B5uYGzKWHyYtpffMoTaP6zNj\nKD9MXMrckLH4B5uop3sz+J6GNOjeigU9JvBe2HiSPtcHGA4O+L4ykmsjJxHZ7ync7gvFOfgOC83U\njVuJevQZoh4bRtJnK/AZZ9mH+IwdRvrOPViT1z/sGDCHjZ3HU+OBe/CsZ9k/pEbEsXv0Yi5b9Q8A\npz5Yx+6Ri2x+ToD6Ic3wCzbxdshYvp+0lAeKyMkDM4ayeuJS3g4Zi59ZTmrd05CG3VvxXo8JzA8b\nz29L1uWXyWuvXg8ZzdeTltC/iPbq6C8HeMtGe3UjMo4vXvqAfWt2FsqJPX0PWl3xCzYxr4S60luv\nK/P0vJi34zuXbeD98Em8Hz7JYtBitDdbmnkzZMZg1g6aw1dF+Oeu/iHcTEjli47jOLR0I+11/1w/\nFcGKnlP45r7JrBn4FqFW7ZU9ff9AeHc+nDvD5j6A3/7cy+WISNavWMZr40cx/e2FACQmJbPok+V8\nvWQ+Xy+Zz6JPlpOYlFykjuK/xT82cLk7rC1bV20F4PTB07h5uuFTxcfimIybGRz98wgA2VnZnD92\nHv9AfwDS9TP9AJVcKiHNLrH16nUvX3y1EoDdew7g5e2FyVTllmNLTk7Jf+7m5mqh3SasLdv0uM8U\nEXfmzQyO/Xk0P+4Lx87jF+gHQGzENf46dQmZW/iSoG/z2qRciiH1ciy5WTlcXrOLave2tDgmNSKO\nhJNXCpUPDGlC9I6jZCakkpWYRvSOowSFNrU4ptt9nflhhdaZHNp/DA8vdyoH+BeKIyUlFQAnJyec\nnZ3zV7oIrl2DPX8cAOD37bvx69kWAPfmdUi/FE3G5RhkVjZxa37H997WFppJfxwjNz1T0z9whgp6\nPm5eiOLmxSgAsmJukBWXiLOfV345pzsbkH31KrlRUZCdTca2rVRs16FQzG5PPkXaN8uRmZn52yq2\n60DGtq2QlUVudDTZV6/idGcDi3KNw1qzZ/UOAC4dPIuLhxuelb0L6V86eJak2IRC2xuHtWLPKm0Q\nemj9Lmq1uyt/351hLTm0WrvqFXHwHJU8XHG30nav7E1FDxciDp7TNFb/xp1hBd95jykD2fTG14VW\nGzE67nrtGgEQ2Kw2CZdiSLyi1cGTP+6iTnfLOli3ewuOrdI+1+n1e7ijvfaZ63Rvyckfd5GTmU3i\nlVgSLsUQ2Ky2VdmWHFv1OwCRB89T0dMNtyqWcbtV8aaiuwuRB7UrmcdW/U7dsFYAXD8XSfyFqEKf\nx9SsNjcuxZCoe+fUj7uoHWYZd+2wFhxfqcV9xizurPQMru49Q/bNrEK6eQQ0q03ipRiSdP2za3dR\ny0q/VlgLTun659btoZqun30zE5mTC4BjRWdsLRxTp3tLjut5iTp4nkpF5KWCuwtRel6Om+VFy+tv\ner5+y99uD+06+nZTXl3Rc3L6x13UscpJnSJynq3nPKeInJcl3wDB97Yk6Uos8Weu2tRvENaSg7o3\nr+je9LDyj4fuzSu6Nw+u/o0G+me/e0A3dixaS05mNgC5+pXQCo3qkx0RSfZVrb1K3bQdl5D2Froy\nteBKmHCpBGb9i0tIO7Ijo8m68FehmH2b1ybZqn+oatU/pEXEkWijfwC49vtxslJu2sxHWXLS0Cwn\n281ykno9Kb9ckzK2V/ERsUSeuoyUuRbb7e37vLyUph1vaObBojDam7Xu1bYH6N7M888ZG/4JvoX2\nyslGe2VP37dq1hgvT48i87Xt9130vq8rQgiaNmpAcnIKsXHx7Ny9n3taN8fL0wMvTw/uad2cnbv3\nF6lTXpFSlutHeaXYgYsQopUQYpsQ4kshRHUhxBYhRKIQYq8QovnfeSM/kx9xUbH5r69Hx+Fn8ivy\neDdPN9p0u5tDOwvOVvQc1JMlvy3lyUlP8tGri/O3Vw0yEXElMv/11YgoqgaZbOouXTqXfXs3M3nS\nGIvtw4cN5vTJncye9T/GjJ1qEff1qIKz39ejr+NbTNyunm606taGIzttn/mzONbkS1rk9fzXaVHx\nuAT6FFOiABeTD2mR8ZZlTZZlAwKrEBUZk/86OvIaAabKNvU+/nYhu05uITUljY1rfwHg7KnzdOsR\nAkCP3t2oGKQNeiqafMm8WpCTzKh4KhSTkyqPdSVh24FC292b1UFUcOLmpej8bY7+/uTGXst/nRsb\ni4O/5WDLqW5dHCpXIXP3LovtDv7+5JiXjYvF0aqsV4APCWY5T4i+jpfJt8jYrfEK8M0vn5uTS0Zy\nGq4+7gB4BviSaKadFB2Pp9V34mnyISmq4HtLiorHM0B7/zu7tyQpJp6Yk5exxui4byan4eLjjrvJ\nh2SzeJKj4vGwitnd5EOSXtek/pldfNzxsC4bHY+7VVkPkw/JZnEnR8fjEWB1TIAPydHFx2CNpltQ\nJiXKhq7ZMTInl0w97lvBzYa+m1VMbjb0K+n6Ac1q89jPs3lsyxtsn/RJ/g8D89iSypAXV39PUq9p\nP/hSryXg6u9pd213q5wkR8XjHlC4riTbqCslUZZ8O7tWpOXw+9k7b3WR+p4BPiSa6RflzUSz+pwY\nFY+n/vn8a5mo2aY+w36YxtMrplChYX0AnCr7kx1d0ObkXIvFsUrhttD9kd4Erfkcn9HPED/nfUAb\nxHgN6U/iYttTllxMvqRfteofSvDF30FrU8w+7y20V4lR8XiZ5SS4TX1G/DCNZ1ZMoVqTWvnHeQf4\ncMOqvfL+G+1VUdjb96B541bqinVezONoOziMFzbM5sE5z1LRbEaBvbzpZvIhxSov1m2xtTet26vH\n9fZqm1V7ZU/fl0RM7HVMVQr68IAq/sTExhETG4epSsFvmYDK2nbF/w9KuuLyATAHWAf8ASyWUnoB\nE/R9NhFCPCuE2CeE2Hc5pfCPsBKDcnTg5QXjWfvJWmIuF/yoXff5Op7p+DSfvvEJj4569G/rDhw8\nkuYtuhES+iAd2rfhiSceyt+36MPPqN+gPRMnz2TSxNF/Wzsv7nELXmbdJz8Sczmm5ALliKGPvEC7\nRvdSoaIz93TUrp5MHD2NAU8+zPc/f4mbuyu5+pm1v4N/v064N63N1Q/WWGx3ruJN3QWjODdmocUZ\nyBIRAvdhz5PyYZHV77bEuVIFOj3fm61zV/7boSgMIObQeb7uNoHv7p9Ky+d7aVdeFHahzdi+HFq6\nkay0DLu9h4OjIy5e7nz4wFQ2zlpO5Tf/97fKp3y7lsg+g7jx3lK8nh4AgNdzg0j6ahUyveirIuWZ\nvJx88MBUNsxazmPvj/q3QyoX7P5yC3M7jeH98IkkX0sgdMoAu72XUSfFYw6dZ3m3CXx7/1RaqfZK\nUc4p6eZ8ZynlBgAhxJtSypUAUspfhBC27/bT9n8EfAQ8f+H4hZYAZ4+cwT+wYITsZ/LnevR1m+VH\nzh5J5KVI1i5bY3P/jrU7GDVnNPv2bgZg375DVKteMOeyarVArkZGFyoXqW9LSUnl629+oHWrZnz5\npeUPxRUr1rBk8dt0bKFdCj135Cx+gQUjfj+TH/FFxD1i9gtEXorkp2Vrbe63Ji06HteggrNzroG+\npEfdKKZEAenRN6hyT8E0KNdAX679eZIBQx/m0YEPAnDk4AkCgwLyjzEFVSEmOraQVh6ZGZn8vOFX\nuvbozM5fd3Ph3CWefES7F6lmrTsYGtYVgIzoeCpULchJhUBfMm3kxKtjE6qN7sfxB6cgzQY9ju4u\nNPhyMpdnLyflwFmLMjlxcThULpjm51C5MrlxBWdShKsrTsHB+Mydr+339cVr+iwSp0wiNy4OR/Oy\n/pXJiYuj48Aw7nlMi/3y4fN4m+Xc2+RHotnZrZJIjInHO8iPhOh4HBwdcPP3YvBXkwC4evgCXmba\nniZfkqItv8+k6Bt4BhacefQM9CUpJh6fGgF4V6vMiA1v5Jcdte0dEq/dICc7x/C4K3m4kn4jhZTo\nG3iYxeMR6EuyVcwp0TfwDPIlJToe4ehARb1ssnVZky8p0TdoPqgbTfqHAhB15AIeZnF7mHxJjrHU\nT465gYep+BisSY6+gUdQQRn3QBu6+jF5cVfQ474VUm3op1rFlHdMqpn+TSv9G+ciyUq9iV/9atRp\nUTs/L9FHLuAZ5EfexKa/m5e0uCTcqniTei0Btyre5GRmM3j9TLtop8VpU4BSrHLiEehLSkzhuuJh\no66URFnyHdC8DrXD29BuUn8qempTfbNvZnGHyKX1Y1q+Iw5fwMtMvyhvepnVZ69AX5L0z5cYHc/x\nTXt1rfPIXImDtxfZsXE4mU1LdqxSmZxrtvsHgLRN2/CbOJrrQMXGDXDr1gmf0c/g4OGOzM3Vpr7O\n164kp0fH41LVqn8owRclUWdIdzoODNE/xwW8g3zJm6TmdQvtlVegL4l6TpKsclLBtRKTNr1Fbk4u\nfx0+j49Ve5XwN9qrorCX7+8e2J1Wel25eot1xToveXGkxhVMmdv3zVaeW/W63byZrk/PS42+gbtV\nXlJstOO30l5l6u1VhL4Qij19XxIBlf2IvlbQ/8dciyOgsj8Blf3Ze/BIwfbYOFo3b1Lm91PcHpR0\nxeWmECJMCPEwIIUQDwAIIToDObeg//6oHiMZ1WMkf27aRZd+XQCo37w+acmp3LhWuBF+4qWBuHq4\nseS1jyy2B9UsGJi07tqay2cv599kv3btJgYO0K6e3N2mBUmJSUSbXb4HcHR0xM9Pu7zp5OREz57d\nOH5cu+G3Tp3g/ON6hnfj+IkzjO0xmrE9RrN70y5C9bjrNa9PWnKazbgff+kJXD3c+Pi1JbeQFo34\nQxfwCDbhVr0yDs6O3NGnLRGbb22eZtT2I5g6N8bZyxVnL1dMnRsTtf0IX338Hb1DH6d36OP8vGE7\nDzzaE4BmLRuRnJRCbIzl5VRXN5f8+14cHR0J6d6BC2cvAeDrr+VLCMGIsU8R87k2UEw5dA6X4EAq\nVq+CcHbCv08H4jdZrkLi1iiY2nOe49Tg2WSZzX0Wzk7U/3g8sd9t5/o6y6leANmnTuFUtRoOJhM4\nOVExtAsZfxTcoClTU4nr24frA/pzfUB/sk6cIHHKJLLPnCbjj51UDO0Czs44mEw4Va1G9qmT/PbF\nZuaEv8Kc8Fc4snkvbfp2AqBm87rcTE6zOce6KI5t2Uebfp21nIa35ey2QywKn8Si8Emc2ryPZn07\nAlCteR1uJqeTYqWdEptARnI61ZprK6M069uRU5v3c+30Fea0GsG8DmOY12EMSdHxvBc6jjfCXrJP\n3H9oixpEHb6AT7AJL70ONujVlnNbLKf1nfv5AI36aZ+rfngbLv9xQtu+5QANerXFsYITXtUr4xNs\nIurQeQ5+/jOfhE/mk/DJnN28n0b9tHuUgprXJiM5LX+qQx6p1xLISEknqLl2f0yjfh04u6V4H0Rb\nxX1nr7act4r7/JYD3PWQFne98DZc0eO+FWIOX8CrpgkPXb9u77ZctNK/uOUAd+r6dXq2IWKnpu9R\nvXL+za0eVf3wqRNE0pVYDn7+M5+FT+YzPS936XkJLCYvmSnpBOp5uatfB87peTH/Thr168ixVb/Z\nTfu8vj368AW8g0146jmpfws5v3yLOS9Lvlf3m87n7V7k83YvcnjZJvYvXMvRz7aw+4stLAyfxMLw\nSZzcvI/mujerN69DRnI6yVb+Sda9WV33ZvO+HTmpt8cnN++jVtuGAPgFmxDOTuQmJJJ5/DRO1avi\nFKS1V273hpD+q+WN8k7VC26od+l4N1lXIrTP/NSLXL3/Ca7e/wRJy1eT9PHXJK8oOFlnq3+4uqls\n8/jPfbqFBeGTWBA+iRNWObn5N3Ny3Cwn/sEmsjOymHXvy8y20V6l/832qijs5fvdX2zJv5n+hFU7\nnnGL7XheXszvh2l4b2sidp+ymzcv6O8Zc/gC3jULvFnvb/jHs4j2yjzn9vJ9SYR0aMvajb8gpeTw\nsZO4u7tR2d+X9ne35I89B0hMSiYxKZk/9hyg/d0tSxYsZ8hy/q+8Ioq7AUcI0RRtqlgu8CIwHBgM\nXAWekVIWXsrEivvv6Jn/BsOmD6dliLas8PyX5nHuiHZj23sbFjCqx0j8TH58tudzrpy9QlamdjPX\nT5/9yOZvNvPsa8/StEMzcrJySElM4cOpi/hoR8GVjffencm9YSGkpafz9NNj2X9AG43v27uZVq3D\ncHV1YdvW1Tg7O+Ho6Mgvv/zGSy+/Tm5uLnPfeZ2uXTuSlZVNwo1ERo2ZTO34gvmZz04fRvOQFtpy\nyC+9y3k97rkb3mVsj9H4mfxYuudTIsziXv/ZOn7+ZjN1mtTllSWTcPdyJysjkxuxCewMKVjdK7BL\nU1q8ri93+c2vnHhvDY1f7kf84Ytc3XwA36a16LjsRSp4u5JzM4ubsYmsD30FgFr9O9NwZG8Ajr+3\nhosrdjA1y3K541fffIVOoe1IT7/JhFGvcezwSQDWbltO79DH8avsy0dfzadChQo4OAh27dzHrP/N\nJScnh8HPPsaAoQ8DsHndNhq/UbAEoneXFgRP01YfiflmK1ffXUX1l/uTcvgcNzbvo+GKV3FtcAdZ\n+pmZjKtxnBoyG/9+nagz73nST1/J1zo7ZiFBsmDgU6HN3bg/PxLh4ED6hvWkLf8StyFDyTp9isw/\nLauc9zvzSVm8qGA55MefwKVHODInh5QPFpK5Zzczzlve7/TwtKE06NyUzPRMvnp5EVeOXgBg/Po3\nmROu5bb3hAG06tMezwAfkmJu8OeKrWyYvxKnis4MnPsC1e6qSVpCCqtHLuSGWSPfc9oQ6nZuoi2H\n/PJiIvWzVsPXz2JRuHZlJqhxMA/qy2ie3X6Yda9+hjUv/j6fxb3+R6TZcshGxv3pyHdxuax9N7VC\nm9Jl6hPa8qLf/squhWvpMLYf0Ucucu7nAzhWdKbnvGEE3FWTmwkprH1hIYn6Z277Qm8aP9IZmZ3L\nL9O+4OJ2zXfmd3R0nz6YWnpO1r/0Uf6Sxk+un8kn4dqKQqbGwfR851lt+c/th9miL5tZ795WdHt9\nEK6+HmQkpRFz4i9WD5wDQHBoU0JffQIHRweOrviV3QvX0n5sP6KPXuT8Fi3u8PnDqKLH/dMLC0m8\nrMX9zM55VPBwwdHZiYykNFY+MZvrZyOpZNYc1ghtSsfXtLycWPEr+xespc24flw7cpFLun73+cPw\nb1STjIQUNj2/kKTLsdTv254WI3qRm52DzJXsffd7Lm7aT5rVaaJu0wcT3LkJ2emZbDDLy+D1M/nM\nLC899Lxc3H6Yn/W8VPJ2p88HI/EM8iPxahxrRyzgZmKqXbTXDS/QDg5tSoie82N6ztuN7UeMWc57\nmOV8nVnOn7aR89QzBfcmljbf5rR5sS9ZaTc5uHg9UY6W9xX1mjaEup2bkpWeweqXF3NVz8kL62ex\nUPdm1cbB9Ht7GE66N3989VMAHJ0d6TvnOQIb1iAnKxvHBe9zc6+2HH+l9m205ZAdHEhZu5GkZcvx\nGjaYzBNnSN/xJz4vjaDS3S0gO5vcpBTi31xQ6GZ8r+cGIdPSSfriO3ZFFVwlD+zStGA55G9+5eS7\na2ik9w+Rev/Q/mPL/mFjiNYWdPlhCh51gnByrUTmjRT2jvuI6O1HOVSxoJL3njaEenpOVprlZOT6\nWSwwy8lDbw/Tlv3dfpi1ZjnpZ5aT9TO/4vCfBcuhP6K3V1npmXz58iIu6+3VhPVvMltvr/ro7ZVX\ngA+Jenu1fv5K7mhSm2cWj8PVy43sjCySYhP4qtvE/DpotO8THCzryv16XjL1upLXjj+/fhbvm7Xj\n/czy8pOel4fmDsfUsAZIuBERy9ZJn1gMToz05ubhC8hI0LyZ5x8H3T/7Fqzlbt0/F838U1n3z0az\n9qqlVXt1YdN+Ms0WiDTa90+sGAbAy6/OZu/BIyQkJOHn682IpwaSna3Nznj0wZ5IKZk59wN+37UP\nl0qVmD7pRRo1qAfA6p82seTzFQA8O7g/D/YMy4/X2b+W5fKW5ZSKlaqX39EBkHHzSrnMY7EDFwAh\nRAMgCNgtpUwx236flHJjSW9gPnAxmo1mf8fFaHqZWthN+5Fsz5IPKiXWAxcj+cyppt206zS03411\n1gMXI/G2859CSuDv31d0q1ST9pvHnFvyIaXGyY5NfSU7alsPXG4XHO0JGdPuAAAKtklEQVSYk4p2\n1LYeuBjJs77XSj6olJgPXOzBITsmPfmWJmKUjpq59muvrAcuRuIp7Wd8Fzs2tJl2/Mk66sA0+4mj\nBi5GUV4HLiWtKjYK+B4YCRwTQvQx2z3LnoEpFAqFQqFQKBT/Rf7t5Y5v1+WQSzpd/AzQSkqZIoSo\nCawUQtSUUr4LlMuRmEKhUCgUCoVCofjvUdLAxSFvepiU8pIQIgRt8FIDNXBRKBQKhUKhUCgU/xAl\nTb6MEUI0y3uhD2LuB/yBxvYMTKFQKBQKhUKh+C/yb08Fu12nipU0cBkEWPxBFClltpRyENDJblEp\nFAqFQqFQKBQKhRnFThWTUkYUs29nUfsUCoVCoVAoFAqFwkjsu5arQqFQKBQKhUKhsKD8TsYq39ym\nf1lAoVAoFAqFQqFQ/H9CDVwUCoVCoVAoFApF+effXrXAxioGzyptpa20y5++0lbaSrv8ad/OsStt\npf1va6vH7fcoj1dcnlXaSltpl0t9pa20lXb507a3vtJW2v9lbcVtRnkcuCgUCoVCoVAoFAqFBWrg\nolAoFAqFQqFQKMo95XHg8pHSVtpKu1zqK22lrbTLn7a99ZW20v4vaytuM4SUaiVphUKhUCgUCoVC\nUb4pj1dcFAqFQqFQKBQKhcICNXBRKBQKhUKhUCgU5Z5yM3ARQtwnhDgthDgnhJhgsPbHQohrQohj\nButWF0JsE0KcEEIcF0KMNli/khBijxDisK7/usH6jkKIg0KIn4zU1bUvCSGOCiEOCSH2GaztLYRY\nKYQ4JYQ4KYS4xyDd+nq8eY8kIcQYI7R1/Rf17/GYEOJrIUQlA7VH67rHyxqzLb8IIXyFEFuEEGf1\n/30M1H5YjztXCNHKDrG/pdeVI0KI74UQ3gZqT9d1DwkhNgshgozSNts3TgghhRD+Bsb9mhDiqlld\nDzcybiHESD3nx4UQcwyMe4VZzJeEEIcM1G4mhNiV12YJIdoYqN1UCPGn3ib+KITwLKW2zT7HCH8W\no11mfxajXWZvFqNdZm8WpW22v9TeLCbuMnuzuLgN8mZRsZfZn8VoG+JPxX+Af/sPyej32DgC54Fa\nQAXgMNDQQP1OQAvgmMFxBwIt9OcewBmD4xaAu/7cGdgNtDVQfyywHPjJDt/pJcDfTvXlM+Bp/XkF\nwNsO7+EIRAM1DNKrClwEXPTX3wJDDNJuBBwDXAEn4GegThn0CvkFmANM0J9PAN40ULsBUB/YDrQq\nYy5s6YcBTvrzNw2O3dPs+SjgQ6O09e3VgU3AX6X1UxFxvwa8ZEDds6UdqtfBivrrKkbmxGz/O8BU\nA+PeDPTQn4cD2w3U3gt01p8PBaaXUttmn2OEP4vRLrM/i9EuszeL0S6zN4vS1l+XyZvFxF1mbxaj\nbZQ3S/ztU1p/FhO7If5Uj9v/UV6uuLQBzkkpL0gpM4FvgD5GiUspdwDxRumZ6UZJKQ/oz5OBk2g/\nUI3Sl1LKFP2ls/4wZDUFIUQ1oCew1Ai9fwohhBfaD4NlAFLKTCllgh3eqitwXkr5l4GaToCLEMIJ\nbZARaZBuA2C3lDJNSpkN/Ar0La1YEX7pgzZgRP//AaO0pZQnpZSnS6N3i/qb9bwA7AKqGaidZPbS\njVL6s5g2ah4wvrS6JWiXmSK0hwOzpZQZ+jHXDNQGQAghgEeArw3UlkDelRAvSunPIrTrATv051uA\nfqXULqrPKbM/i9I2wp/FaJfZm8Vol9mbJfTxZfKmPX8/FKNtlDeLjb0s/ixG2xB/Km5/ysvApSpw\nxex1BAYOAP4JhBA1geZoV0WM1HXUL7deA7ZIKY3Sn4/W6OYapGeNBDYLIfYLIYz8q7fBQCzwidCm\nuS0VQrgZqJ9Hf0r5o8gWUsqrwNvAZSAKSJRSbjZI/hjQUQjhJ4RwRTsbVd0g7TwCpJRR+vNoIMBg\n/X+KocAGIwWFEDOFEFeAAcBUA3X7AFellIeN0rTiBX0qzcelmVpUDPXQ6uNuIcSvQojWBmrn0RGI\nkVKeNVBzDPCW/l2+DUw0UPs4BSfjHsYAf1r1OYb60179WQnaZfamtbaR3jTXNtqbNnJimDettA33\nZhHfpyH+tNK2pz8VtxHlZeByWyOEcAdWAWOszvKUGSlljpSyGdqZqDZCiEZl1RRC3A9ck1LuL3OA\nRdNBStkC6AE8L4ToZJCuE9o0jEVSyuZAKtrUCMMQQlQAegPfGajpg/bDJRgIAtyEEE8YoS2lPIk2\nzWIzsBE4BOQYoV3E+0kMuvL3TyKEmAxkA18ZqSulnCylrK7rvmCEpj4AnYSBAyErFgG1gWZoA+l3\nDNR2AnyBtsDLwLf6GVgjeQwDTyzoDAde1L/LF9Gv6hrEUGCEEGI/2vSXzLKIFdfnlNWf9uzPitI2\nwpu2tI3yprm2Hqdh3rQRt2HetKFtqDeLqStl9qcNbXv6U3EbUV4GLlexPANVTd9W7hFCOKOZ6ysp\n5Wp7vY8+HWobcJ8Bcu2B3kKIS2jT8roIIb40QDcf/QpD3qXo79GmAxpBBBBhduVpJdpAxkh6AAek\nlDEGanYDLkopY6WUWcBqoJ1R4lLKZVLKllLKTsANtHnBRhIjhAgE0P8v1RSDfwshxBDgfmCA/sPO\nHnxFKacA2aA22iD3sO7TasABIYTJCHEpZYx+UiQXWIJx/gTNo6v1qa570K7qlmphAVvoUy37AiuM\n0tQZjOZL0E5aGJYTKeUpKWWYlLIl2g+686XVKqLPMcSf9uzPitI2wpu3EHepvWlD2zBv2orbKG8W\nkRPDvFnM91lmfxahbTd/Km4vysvAZS9QVwgRrJ/t7g+s/ZdjKhH9TMUy4KSUcq4d9CsLfZUVIYQL\n0B04VVZdKeVEKWU1KWVNtFxvlVIacvYfQAjhJoTwyHuOdgOmISu6SSmjgStCiPr6pq7ACSO0zbDH\n2dzLQFshhKteb7qizd01BCFEFf3/O9A6jeVGaeusRes40P9fY7C+3RBC3Ic2LbK3lDLNYO26Zi/7\nYIA/AaSUR6WUVaSUNXWfRqDdsBpthH7ej1ydBzHInzo/oN0EjBCiHtoCGnEG6ncDTkkpIwzUBG3O\nfGf9eRfAsGloZv50AP4HfFhKnaL6nDL70579WVHaRnizGO0ye9OWtlHeLCbuMnuzmO/SEG+WUFfK\n5M9itO3mT8VthiwHKwToJ1nC0c4SnwcmG6z9Ndol1yy0RuYpg3Q7oF2SP4I2PecQEG5g3E2Ag7r+\nMUq5gk4J7xGCwauKoa0Od1h/HLfD99kM2Kfn5QfAx0BtN+A64GWHXL+O1nkeA75AX9nFIO3f0AZw\nh4GuZdQq5BfAD/gFrbP4GfA1UPtB/XkGEANsMjj2c2j30OV5tLQrf9nSXqV/n0eAH9FuCjZE22r/\nJUq/qpituL8AjupxrwUCDdSuAHyp5+UA0MXInACfAsPsUMc7APt1D+0GWhqoPRqtfzsDzAZEKbVt\n9jlG+LMY7TL7sxjtMnuzGO0ye7MobatjSuXNYuIuszeL0TbKm0Xmpaz+LCZ2Q/ypHrf/Q+gVRaFQ\nKBQKhUKhUCjKLeVlqphCoVAoFAqFQqFQFIkauCgUCoVCoVAoFIpyjxq4KBQKhUKhUCgUinKPGrgo\nFAqFQqFQKBSKco8auCgUCoVCoVAoFIpyjxq4KBQKhUKhUCgUinKPGrgoFAqFQqFQKBSKcs//Affe\nzZVeusP4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('feature correlations')\n",
    "associations(X, nominal_columns=None, return_results=False, figsize=[15, 15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "colab_type": "code",
    "id": "xxtCzbCWMMez",
    "outputId": "58fe4692-4604-4600-d3d4-50f782074b0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting annoy\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/00/15/5a9db225ebda93a235aebd5e42bbf83ab7035e7e4783c6cb528c635c9afb/annoy-1.16.3.tar.gz (644kB)\n",
      "\r",
      "\u001b[K     |▌                               | 10kB 21.0MB/s eta 0:00:01\r",
      "\u001b[K     |█                               | 20kB 1.7MB/s eta 0:00:01\r",
      "\u001b[K     |█▌                              | 30kB 2.5MB/s eta 0:00:01\r",
      "\u001b[K     |██                              | 40kB 1.7MB/s eta 0:00:01\r",
      "\u001b[K     |██▌                             | 51kB 2.1MB/s eta 0:00:01\r",
      "\u001b[K     |███                             | 61kB 2.5MB/s eta 0:00:01\r",
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      "\u001b[K     |████████████████████████████████| 645kB 2.8MB/s \n",
      "\u001b[?25hBuilding wheels for collected packages: annoy\n",
      "  Building wheel for annoy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for annoy: filename=annoy-1.16.3-cp36-cp36m-linux_x86_64.whl size=297343 sha256=4b2ad2315a04bf8bef1e925d7db182631066322a55e6910da1bf160fdcecec13\n",
      "  Stored in directory: /root/.cache/pip/wheels/f3/01/54/6ef760fe9f9fc6ba8c19cebbe6358212b5f3b5b0195c0b813f\n",
      "Successfully built annoy\n",
      "Installing collected packages: annoy\n",
      "Successfully installed annoy-1.16.3\n"
     ]
    }
   ],
   "source": [
    "pip install annoy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "U8gSzMPyMLW0",
    "outputId": "ccad2364-cb3f-41bf-b1f5-97577dacc833"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from annoy import AnnoyIndex\n",
    "t = AnnoyIndex(X.shape[1], 'euclidean')  # Length of item vector that will be indexed\n",
    "for i, v in enumerate(X):\n",
    "    t.add_item(i, v)\n",
    "\n",
    "t.build(10) # 10 trees\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "colab_type": "code",
    "id": "5-I8RH-LKE8F",
    "outputId": "98623720-8ec8-4fda-cf5a-3fc14bbe0755"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mpld3\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/95/a52d3a83d0a29ba0d6898f6727e9858fe7a43f6c2ce81a5fe7e05f0f4912/mpld3-0.3.tar.gz (788kB)\n",
      "\u001b[K     |████████████████████████████████| 798kB 2.8MB/s \n",
      "\u001b[?25hBuilding wheels for collected packages: mpld3\n",
      "  Building wheel for mpld3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for mpld3: filename=mpld3-0.3-cp36-none-any.whl size=116679 sha256=76e00c56b559a25f38522475dd2ad00922e99e0be8b0459000b1db8c14e7d9cf\n",
      "  Stored in directory: /root/.cache/pip/wheels/c0/47/fb/8a64f89aecfe0059830479308ad42d62e898a3e3cefdf6ba28\n",
      "Successfully built mpld3\n",
      "Installing collected packages: mpld3\n",
      "Successfully installed mpld3-0.3\n"
     ]
    }
   ],
   "source": [
    "pip install mpld3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "kvUCm-JvNmLH",
    "outputId": "f941869a-efc0-4d30-9a67-fbb0e4a2a4f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe723480048>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el140140630846553224324700522\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el140140630846553224324700522\", {\"width\": 432.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-0.9653128623962403, 20.271570110321044], \"ylim\": [0.0, 128.1], \"xdomain\": [-0.9653128623962403, 20.271570110321044], \"ydomain\": [0.0, 128.1], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 11, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el140140630706094152\", \"lines\": [], \"paths\": 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el140140630846553224324700522\", {\"width\": 432.0, \"height\": 288.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-0.9653128623962403, 20.271570110321044], \"ylim\": [0.0, 128.1], \"xdomain\": [-0.9653128623962403, 20.271570110321044], \"ydomain\": [0.0, 128.1], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 11, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": null, \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el140140630706094152\", \"lines\": [], \"paths\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el140140630705314280\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el140140630705313720\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data02\", \"xindex\": 2, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"id\": \"el140140630705315680\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#1F77B4\", \"edgecolor\": \"none\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data02\", \"xindex\": 3, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import mpld3\n",
    "mpld3.enable_notebook()\n",
    "\n",
    "_, distances = t.get_nns_by_item(0, 10000, include_distances=True)\n",
    "pd.Series(distances).hist(bins=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "vCvWX-kgeio4",
    "outputId": "efe17c3e-bda2-4139-bc32-16138e98b1b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 29)"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "AvHGgAiKMxy6",
    "outputId": "26910e1b-087b-4f50-c41b-8e27e87c9d67"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|████▏     | 119170/284807 [29:15<48:59, 56.35it/s]"
     ]
    }
   ],
   "source": [
    "from tqdm import trange\n",
    "from scipy.sparse import lil_matrix\n",
    "\n",
    "MAX_NEIGHBORS = 10000  # Careful: this parameter determines the run-time of the loop!\n",
    "THRESHOLD = 6.0\n",
    "\n",
    "def get_neighbors(i):\n",
    "  neighbors, distances = t.get_nns_by_item(i, MAX_NEIGHBORS, include_distances=True)\n",
    "  return [n for n, d in zip(neighbors, distances) if d < THRESHOLD]\n",
    "\n",
    "\n",
    "n_rows = X.shape[0]\n",
    "neighborhood = dict()\n",
    "for i in trange(n_rows):\n",
    "  neighborhood[i] = get_neighbors(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V1Oh4IMAgxiw"
   },
   "outputs": [],
   "source": [
    "A = lil_matrix((n_rows, n_rows), dtype=np.int8)\n",
    "\n",
    "for i, n in neighbors.items():\n",
    "  A[i, n] = 1.0\n",
    "  A[n, i] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nayd6MDDSNL0"
   },
   "outputs": [],
   "source": [
    "print('max sparsity given max neighbors parameter: {}'.format(MAX_NEIGHBORS / n_rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7BZ-1QniSHqu"
   },
   "outputs": [],
   "source": [
    "print('average number of connections: {}'.format(A.sum(axis=0).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OOUfItyTK_yD"
   },
   "outputs": [],
   "source": [
    "print('sparsity: {}'.format(A.sum() / (n_rows * n_rows)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y8eJ3vIxmGrO"
   },
   "outputs": [],
   "source": [
    "# another attempt: I used the previous one in the experiment reported in the book\n",
    "from numba import njit, jit, prange\n",
    "import numpy as np\n",
    "from numba.pycc import CC\n",
    "from scipy.sparse import lil_matrix\n",
    "from scipy.spatial.distance import cosine\n",
    "\n",
    "\n",
    "cc = CC('adjacency_utils')\n",
    "\n",
    "def angular(u, v):\n",
    "  return np.sqrt(2 * (cosine(u, v)))\n",
    "\n",
    "@cc.export('calc_dist', 'f8(f8[:], f8[:])')\n",
    "@jit(\"f8(f8[:], f8[:])\")\n",
    "def calc_dist(u, v):\n",
    "    '''Euclidean distance (without sqrt)\n",
    "    Example:\n",
    "    --------\n",
    "    >> calc_dist(X[0, :], X[1, :])\n",
    "    12.795783809844064\n",
    "    '''\n",
    "    d = u - v\n",
    "    return np.sum(d * d)\n",
    "\n",
    "@jit(nopython=False, parallel=True, forceobj=True)\n",
    "def calculate_adjacency(X, threshold=0.5):\n",
    "    '''Calculate an adjacency matrix\n",
    "    given a feature matrix\n",
    "    '''\n",
    "    n_rows = X.shape[0]\n",
    "    A = lil_matrix((n_rows, n_rows), dtype=np.int8)\n",
    "    for i in prange(n_rows):\n",
    "        for i2 in range(i+1, n_rows):\n",
    "            d = calc_dist(X[i, :], X[i2, :])\n",
    "            if d < threshold:\n",
    "                A[i, i2] = 1.0\n",
    "                A[i2, i] = 1.0\n",
    "    return A\n",
    "\n",
    "cc.compile()\n",
    "\n",
    "# too slow, don't run!\n",
    "#A = calculate_adjacency(X, threshold=threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4WnxYs_8Osn5"
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "G = nx.from_scipy_sparse_matrix(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse.csgraph import connected_components\n",
    "n_components, labels = connected_components(A, directed=False, return_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XFEIfk2ERx7q"
   },
   "outputs": [],
   "source": [
    "len(dists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BzQlXZF-QlJY"
   },
   "outputs": [],
   "source": [
    "nx.draw(G, with_labels=True, font_weight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SHhjNe6EM28Q"
   },
   "outputs": [],
   "source": [
    "t.get_distance(0, 64172)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wzR9T5HbNBB5"
   },
   "outputs": [],
   "source": [
    "t.get_distance(0, 212379)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "15l8EJJsJsV6"
   },
   "outputs": [],
   "source": [
    "transaction_correlations = associations(X.transpose(), nominal_columns=None, return_results=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0O1Lm-0iJWJr"
   },
   "outputs": [],
   "source": [
    "correlations.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hbgwx4enIKn8"
   },
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist\n",
    "# https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.spatial.distance.pdist.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hy2Jh1FuIRDG"
   },
   "outputs": [],
   "source": [
    "correlations = pdist(X, metric='euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j2tpaagjG-Ch"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import pairwise_distances\n",
    "\n",
    "correlations = pairwise_distances(X, metric='euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XRIDMwjKH-6G"
   },
   "outputs": [],
   "source": [
    "correlations.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ze9XMV3gG6dV"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8hbhZucvD9gw"
   },
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fI48SzT0D2JS"
   },
   "outputs": [],
   "source": [
    "graph = nx.convert_matrix.from_pandas_edgelist(ata, \"id_1\", \"id_2\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "Graph community detection for fraudsters",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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